Mirror-based agent training system

Abstract

The application relates to the technical field of agent training and discloses an agent training system based on mirroring. The system comprises a training environment mirroring module, a behavior feature reconstruction module and a training decision model module. The training environment mirroring module constructs a virtual mirroring environment which is completely synchronized with a real physical environment, covers an environment state parameter set, an agent interaction record sequence and a performance index time sequence, and can reproduce a real environment complex dynamic scene. The behavior feature reconstruction module analyzes a three-dimensional behavior trajectory of the agent interaction record sequence, generates a behavior feature tensor containing a decision response delay gradient, an action space coverage degree and a cooperation intention fluctuation coefficient, and realizes multi-dimensional behavior analysis. The training decision model module calls a pre-trained agent evolution model, maps a behavior feature tensor in a strategy space, generates an optimized strategy parameter set and a behavior defect area identifier, and helps the agent to efficiently play a role in a real scene.

Description

Technical Field

[0001] This invention relates to the field of intelligent agent training technology, specifically to an intelligent agent training system based on mirroring. Background Technology

[0002] In the field of intelligent agent training, constructing a training environment that closely resembles real-world scenarios is a crucial prerequisite for achieving efficient training. Traditional intelligent agent training often employs virtual environments with pre-defined rules. These environments frequently fail to fully replicate the complex variables and dynamic changes inherent in real-world physical environments, leading to insufficient adaptability of the trained agents when applied in real-world scenarios. For example, in training industrial robot agents, traditional virtual environments may ignore the impact of temperature and humidity in a real workshop on the robot's motion accuracy, and cannot synchronously simulate the random scheduling of material transport during production. This results in a significant decrease in the robot agent's action accuracy and task response efficiency during actual operations.

[0003] Traditional agent training lacks depth and comprehensiveness in its analysis of agent behavior characteristics. Most existing technologies focus only on single behavioral parameters, such as action execution frequency, while neglecting crucial features like decision-response latency, action space coverage, and fluctuations in collaborative intent. In multi-agent collaborative scenarios, this one-sided behavioral analysis leads to an inability to accurately assess the coordination between agents, making it difficult to formulate effective optimization strategies. For example, in a multi-robot collaborative system for intelligent warehousing, the inability to accurately obtain the decision-response latency gradient and collaborative intent fluctuation coefficient of each robot can easily lead to path conflicts and task allocation imbalances when multiple robots simultaneously perform cargo handling tasks, severely impacting the overall operational efficiency of the warehousing system.

[0004] Traditional agent training models have limitations in policy generation. Existing pre-trained models often use simple linear mapping to process agent behavioral features, failing to fully explore the complex relationship between behavioral features and optimization strategies. This results in insufficiently targeted parameter sets for generated optimization strategies and difficulty in accurately identifying behavioral deficiencies in the agent. Consequently, the agent cannot accurately improve based on its own behavioral shortcomings during training, leading to prolonged training cycles and unsatisfactory training results. For example, in training autonomous driving agents, traditional models cannot accurately generate optimized driving strategies adapted to different weather conditions and traffic flows based on the agent's behavioral characteristics under complex road conditions, nor can they accurately identify behavioral deficiencies in emergency obstacle avoidance decisions. This results in significant safety hazards for autonomous driving agents in real-world road tests. Summary of the Invention

[0005] The purpose of this invention is to provide a mirror-based intelligent agent training system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a mirror-based intelligent agent training system, the system comprising:

[0007] The training environment mirror module is used to create a virtual mirror environment that is completely synchronized with the real physical environment. The virtual mirror environment includes a set of environmental state parameters, a sequence of agent interaction records, and a time series of performance indicators.

[0008] The behavior feature reconstruction module is used to perform three-dimensional behavior trajectory parsing on the interaction record sequence of the agent to generate an agent behavior feature tensor. The agent behavior feature tensor includes decision response delay gradient, action space coverage and cooperative intention fluctuation coefficient.

[0009] The training decision model module is used to call the pre-trained agent evolution model to perform policy space mapping processing on the agent's behavioral feature tensor, and generate the target agent's optimized policy parameter set and behavioral defect region identifier.

[0010] Preferably, the training environment mirror module includes:

[0011] The dynamic mirror synchronization unit updates the physical constraints and dynamic obstacle distribution data in the environmental state parameter set in real time based on the sensor data stream in the real physical environment.

[0012] The interaction record analysis unit extracts the action command timestamps and environmental feedback delay data from the agent's interaction record sequence, and generates an interaction event chain containing time alignment markers;

[0013] The performance metric tracking unit divides the performance metric time series into multiple metric subsequences according to task type, and each metric subsequence is associated with a task execution stage.

[0014] Preferably, the behavioral feature reconstruction module includes:

[0015] The spatiotemporal trajectory segmentation unit is used to divide the interactive event chain into multiple behavioral segments according to the decision cycle, and each behavioral segment corresponds to a decision response window;

[0016] A three-dimensional feature coupling unit is used to perform the following processing for each behavior segment: constructing a three-dimensional environmental topology mesh based on the set of environmental state parameters, performing spatiotemporal correlation analysis between the three-dimensional environmental topology mesh and the action command timestamp, and generating behavior feature components of the current decision response window, wherein the behavior feature components include a displacement response matrix, an action selection probability distribution, and an intent recognition vector.

[0017] The feature accumulation unit aggregates and calculates the behavioral feature components of multiple consecutive decision response windows, and outputs the decision response delay gradient, action space coverage, and cooperative intention fluctuation coefficient.

[0018] Preferably, the training decision model module includes:

[0019] The strategy optimization unit is used to input the decision response delay gradient into the first strategy layer of the agent evolution model and generate strategy response time optimization parameters through a response delay analysis algorithm.

[0020] The behavior correction unit is used to input the action space coverage into the second policy layer of the agent evolution model, perform action space exploration efficiency calculation, and generate action coverage blind zone coordinates and action repetition probability distribution.

[0021] The cooperation reinforcement unit is used to input the cooperation intention fluctuation coefficient into the third strategy layer of the agent evolution model and generate cooperation protocol update parameters based on the intention stability evaluation algorithm.

[0022] Preferably, the system further includes:

[0023] The environmental disturbance compensation module extracts records of environmental abrupt events from the set of environmental state parameters and calculates the drift amount of the virtual mirror environment parameters;

[0024] An environment drift correction unit performs dynamic calibration on the three-dimensional environment topology mesh based on the drift amount of the environmental parameters, and generates a calibrated three-dimensional environment topology mesh.

[0025] The strategy drift suppression unit performs anti-interference enhancement processing on the optimized strategy parameter set based on the correlation between the frequency of environmental mutations and the stability of the strategy, thereby generating a more robust strategy parameter set.

[0026] Preferably, the environmental drift correction unit includes:

[0027] A drift detection subunit is used to identify the environmental attribute mutation values ​​and mutation durations in the environmental mutation event records.

[0028] The mesh reconstruction sub-unit is used to adjust the topological connection weights of the three-dimensional environment topological mesh according to the abrupt change value of the environmental attribute, and generate a topological structure update matrix;

[0029] The time-compensation subunit is used to perform time-decay processing on the topology update matrix based on the duration of the mutation, and output the calibrated three-dimensional environmental topology mesh.

[0030] Preferably, the system further includes:

[0031] The training efficiency optimization module generates a strategy adjustment instruction set based on the behavioral defect area identifier. The strategy adjustment instruction set includes decision frequency adjustment parameters, action space expansion scheme, and cooperation protocol enhancement strategy.

[0032] The decision frequency adjustment unit calculates the optimal decision response time threshold based on the decision response delay gradient.

[0033] The action space optimization unit constructs an action exploration path planning scheme based on the coordinates of the action coverage blind zone.

[0034] The collaborative enhancement execution unit generates multi-agent communication rule adjustment instructions based on the updated parameters of the collaborative protocol.

[0035] Preferably, the motion space optimization unit includes:

[0036] The blind spot analysis subunit extracts the spatial distribution characteristics of the action-covered blind spot coordinates and calculates the blind spot aggregation degree and boundary curvature.

[0037] The path generation subunit determines the action exploration path density based on the blind zone aggregation degree, and the action exploration path density is positively correlated with the blind zone aggregation degree.

[0038] The direction control subunit adjusts the action exploration direction angle based on the boundary curvature, so that the exploration direction forms a preset angle with the blind zone boundary.

[0039] Preferably, the system further includes:

[0040] The model validation module is used to collect the actual decision-making trajectory and collaborative efficiency indicators of the target intelligent agent within a preset validation period.

[0041] The strategy error analysis unit is used to compare the actual decision trajectory with the predicted behavior trajectory to generate a strategy execution error coefficient.

[0042] The model parameter update unit is used to adjust the evolution rate parameter of the agent evolution model according to the strategy execution error coefficient, and generate an optimized agent evolution model.

[0043] Preferably, the model verification module includes:

[0044] The dynamic sampling unit dynamically adjusts the verification cycle duration based on the rate of change of the collaborative performance index.

[0045] The feedback fusion unit performs correlation analysis between the strategy execution error coefficient and the cooperation protocol update parameters to generate the model weight update amount of the fusion feedback.

[0046] The closed-loop optimization unit drives the mirror update frequency of the training environment mirror module based on the model weight update amount of the fusion feedback.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] By setting up a training environment mirroring module, a virtual mirror environment that is completely synchronized with the real physical environment can be created. This virtual mirror environment includes a set of environmental state parameters, a sequence of agent interaction records, and a time series of performance indicators, which can comprehensively reproduce various dynamic variables and complex scenarios in the real environment. During agent training, there is no need to rely on a pre-set virtual environment that differs from the real environment. This allows the agent to be exposed to environmental conditions consistent with real application scenarios during the training phase, avoiding the problem of insufficient adaptability of the agent in real scenarios due to environmental differences. Whether it is training intelligent robots in industrial production scenarios or training autonomous driving agents in the field of intelligent transportation, the agent can fully adapt to various parameter changes in the real environment during the training process, reducing debugging costs and adaptation difficulties in subsequent practical applications.

[0049] The behavioral feature reconstruction module performs three-dimensional behavioral trajectory analysis on the agent interaction record sequence, generating an agent behavioral feature tensor that includes decision-response delay gradient, action space coverage, and collaborative intent fluctuation coefficient. This overcomes the limitations of traditional techniques that focus only on a single behavioral parameter, enabling multi-dimensional and in-depth analysis of agent behavioral features. In multi-agent collaborative scenarios, the decision-response delay gradient obtained by this module clearly reflects the time differences in decision-making stages among agents during task execution, helping to determine the synchronization of decisions between agents. The action space coverage intuitively presents the comprehensiveness of actions taken by agents during task execution, avoiding task omissions due to insufficient action coverage. The collaborative intent fluctuation coefficient accurately captures changes in the collaborative willingness among agents, providing a key basis for judging the coordination of multi-agent collaboration. Based on these comprehensive behavioral features, we can more accurately grasp the behavioral patterns and potential problems of agents, providing rich and accurate reference information for the formulation of subsequent optimization strategies.

[0050] The training decision model module calls the pre-trained agent evolution model to perform policy space mapping on the agent's behavioral feature tensor, generating an optimized policy parameter set and behavioral defect region identifiers for the target agent. Compared to traditional linear mapping methods, this approach more fully explores the complex relationship between behavioral features and optimized policies. The generated optimized policy parameter set is more targeted, capable of formulating adaptive improvement schemes based on the agent's specific behavioral characteristics. This allows the agent to make precise adjustments based on the optimized policies during training, accelerating the training process. Simultaneously, the clear behavioral defect region identifiers directly point out the agent's shortcomings in behavioral performance, enabling trainers to clearly understand the key areas for improvement without relying on extensive testing and analysis to infer the defects. For example, in intelligent logistics multi-robot collaborative training, this module can generate optimized policies for different robot collaboration defects, such as adjusting the decision response speed of one robot or optimizing the motion path planning of another. It also accurately identifies behavioral defects in the cargo handover process, making training more targeted and efficient, and improving the overall training quality of the agent.

[0051] The various modules of this system work together to form a complete and efficient intelligent agent training system. The training environment mirroring module provides a realistic and comprehensive interactive data foundation for behavioral feature reconstruction, while the accurate behavioral feature tensors generated by the behavioral feature reconstruction module provide a reliable basis for policy generation in the training decision model module. These three elements support each other, forming a closed loop in the entire training process. This synergistic effect not only enhances the individual performance of each module but also improves the overall training efficiency and quality. It can meet the needs of intelligent agent training in different fields and scenarios, promoting the further development and popularization of intelligent agent technology in practical applications. Attached Figure Description

[0052] Figure 1 This is a timing diagram of the mirror-based intelligent agent training system described in this invention.

[0053] Figure 2 A flowchart illustrating the operation of the training environment mirror module;

[0054] Figure 3 A flowchart illustrating the workflow of the behavior feature reconstruction module;

[0055] Figure 4 A flowchart illustrating the operation of the environmental disturbance compensation module;

[0056] Figure 5 A flowchart illustrating the work of the training performance optimization module. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1 This invention provides a mirror-based intelligent agent training system, the system comprising:

[0059] The training of the agent is achieved by creating a virtual mirror environment that is fully synchronized with the real physical environment. The training environment mirror module is responsible for generating this virtual mirror environment, which includes a set of environmental state parameters, a sequence of agent interaction records, and a time series of performance metrics. The set of environmental state parameters captures the physical properties of the real physical environment, the agent interaction records record the interaction events between the agent and the environment, and the time series of performance metrics quantifies the agent's performance at different task stages. The behavior feature reconstruction module performs three-dimensional behavior trajectory analysis on the agent interaction records to generate a behavior feature tensor. This tensor includes the decision-response delay gradient, action space coverage, and cooperative intention fluctuation coefficient. The decision-response delay gradient reflects the changing trend of the agent's decision-response time, the action space coverage represents the diversity of the agent's action choices, and the cooperative intention fluctuation coefficient measures the stability of the agent's cooperative intention. The training decision model module calls a pre-trained agent evolution model, which is built based on a reinforcement learning framework, and performs policy space mapping processing on the agent behavior feature tensor. Policy space mapping involves mapping the behavior features to the policy space, generating an optimized policy parameter set for the target agent and identifiers of behavioral defect regions. The optimization strategy parameter set includes strategy weights and decision thresholds. Behavioral defect region identifiers mark inefficient regions in the agent's behavior, such as regions with excessively high response latency or unstable cooperation.

[0060] Example 1: See Figure 2The system processes sensor data streams from the real physical environment in real time through a dynamic mirror synchronization unit. This unit connects to a LiDAR, camera array, and inertial measurement unit deployed in the real environment, continuously receiving point cloud data, video streams, and six-axis motion parameters. The LiDAR scans the environmental spatial structure at a frequency of 10Hz, and the generated point cloud data, after noise reduction, is input into a spatial analysis algorithm. This algorithm extracts terrain elevation changes, fixed obstacle outlines, and traversable area boundaries, updating the terrain constraint coefficients and static obstacle coordinate sets in the environmental state parameter set. The camera array captures RGB-D images at 30fps, identifies moving objects through a target detection model, and calculates the instantaneous velocity vectors of dynamic obstacles using an optical flow algorithm. The position coordinates and velocity vectors of all dynamic obstacles are stored in a circular buffer with timestamps, forming a real-time update stream of dynamic obstacle distribution data. The acceleration and angular velocity data collected by the inertial measurement unit are processed by attitude calculation, outputting a gravity direction vector and vibration frequency spectrum, which are used to update the gravity influence factor and platform stability parameters in the physical constraints.

[0061] The interaction record analysis unit extracts two core types of data from the agent's interaction record sequence: action command timestamps and environmental feedback delay data. Action command timestamps record the precise moment the agent issues control commands, with millisecond-level precision, stored in a triplet format of <action type, coordinate command, timestamp>. Environmental feedback delay data is obtained by comparing the time difference between the action command timestamp and the recorded environmental state change. For example, when the agent issues a "robotic arm grasp" command, the system monitors the data change time of the target object's displacement sensor and calculates the delay in milliseconds from the command issuance to the object's state change. This data is input into a time alignment engine, which uses a hardware clock synchronization protocol to eliminate sensor clock drift and sorts action command events and environmental feedback events along a unified timeline. For each action command event, the engine searches for environmental feedback events within the corresponding time window. When a feedback event is missing, an empty event marker is inserted, ultimately generating an interaction event chain with alignment markers. The chain structure is a quadruple sequence of <action event, feedback event, delay value, alignment state>.

[0062] The performance tracking unit receives raw performance index time series, which contains multi-dimensional evaluation data during the agent's task execution. The unit has a built-in task classifier that categorizes tasks into three types—navigation, collaboration, and decision-making—based on matching rules in a task feature library. For navigation tasks, the classifier detects path planning instructions and position coordinate changes; for collaboration tasks, it identifies them through the frequency of multi-agent communication messages; and for decision-making tasks, it determines them based on the trigger frequency of environmental state assessment requests. The task execution phase segmenter uses a sliding window analysis method, detecting inflection points in performance index changes within a 5-second window: when the standard deviation of navigation accuracy continuously exceeds a threshold, it is marked as an execution phase; when the energy consumption rate suddenly increases, it is marked as a critical operation phase; and when the communication error rate rises, it is marked as a collaboration conflict phase. Each identified task phase generates an independent index subsequence; for example, the initialization phase of a navigation task corresponds to a coordinate offset quantum sequence, and the execution phase of a collaboration task corresponds to a communication delay subsequence. All index subsequences are stored in a time-series database, and a dual index relationship is established with task type and execution phase.

[0063] The dynamic mirror synchronization unit's environment update mechanism comprises a three-layer processing pipeline. The first layer is sensor data preprocessing: LiDAR point clouds are downsampled using voxel grid filtering; camera images are processed using background subtraction algorithms to extract moving targets; and inertial data is low-pass filtered to eliminate high-frequency noise. The second layer is environmental parameter fusion: a static environment builder converts the downsampled point cloud into an octree spatial model; a dynamic obstacle tracker fuses image recognition results with point cloud clustering data to generate obstacle trajectory predictions; and a physical parameter calculator updates the friction coefficient lookup table based on inertial data spectrum analysis. The third layer is real-time synchronization output: the updated environmental state parameter set is output using shared memory mapping technology; physical constraints are stored in a <parameter name, value, update time> structure; and dynamic obstacle distribution data uses a dynamic array structure of <obstacle ID, position coordinates, velocity vector, trajectory prediction point>. The entire synchronization process is completed within a 50-millisecond cycle, ensuring that the time deviation between the virtual mirror environment and the real physical environment is less than 100 milliseconds.

[0064] The generation process of the interactive event chain involves time correction technology. When the time difference between the action command timestamp and the corresponding environmental feedback exceeds a preset threshold, the time alignment engine activates a clock offset compensation algorithm. This algorithm analyzes the clock deviation trend of historical time-series data and establishes a linear regression model of sensor clock drift. For environmental events that are not responded to in a timely manner, the engine activates a timeout re-detection mechanism: sending a data retransmission request to the environmental sensor and copying the most recent environmental state as a temporary filler value. Alignment markers use a three-color identification system: green marks indicate precisely aligned event pairs; yellow marks indicate events aligned after clock correction; and red marks represent missing feedback events. The event chain storage adopts a paginated storage structure, with each page containing 100 event records. The page header stores the time calibration parameters and clock compensation coefficients for that page.

[0065] The performance metric subsequence management system includes a dynamic memory allocator. When a new task phase is identified, the system allocates contiguous storage space from the memory pool and initializes a circular buffer for the metric subsequence. Each buffer header stores the task type code, phase start timestamp, and sampling frequency parameters. Metric data writing employs a double-buffering mechanism: a foreground buffer receives real-time data, and when the buffer is full, a background thread is triggered to write the data in batches to the solid-state storage array. The subsequence retrieval interface supports time range queries and joint queries of task phases; for example, it can retrieve "path offset data for the first 30 seconds of the navigation task execution phase." All subsequence metadata is registered in a global hash table, with the hash key generated by combining the task type ID and phase ID, achieving sub-millisecond retrieval response times.

[0066] The persistent storage of the environment state parameter set adopts a differential update strategy. Basic environment parameters are fully updated only when a change in the environment structure is detected, while dynamic parameters use an incremental update mechanism. Each update generates a parameter snapshot with an incrementing version number, and historical versions are stored in a time chain, supporting accurate restoration of historical environment states during training replay. The parameter access interface provides version control functionality, allowing the training module to specify a timestamp to retrieve the corresponding environment state image.

[0067] The event compensation module of the interactive recording and analysis unit handles sensor data loss scenarios. When the corresponding environmental feedback data is missing from the action command timestamp, the compensation module initiates a simulation based on physical laws: it calls a preset physical response model according to the action type; for example, a rigid body mechanics model is called for a "pushing" action, and a heat conduction equation is called for a "heating" action. The simulation result generates a simulated environmental feedback event, and a simulation identifier is added to the event tag. At the same time, the unit maintains a data loss counter, and when the loss rate of a specific type of action exceeds a threshold, a calibration alarm signal is sent to the sensor system.

[0068] The task stage segmenter in the performance tracking unit employs multi-feature fusion decision-making. The segmenter simultaneously monitors three types of features: operational rhythm features (e.g., changes in action command intervals), environmental response features (e.g., object state change rates), and resource consumption features (e.g., the slope of the energy consumption curve). A weighted decision tree model is used to comprehensively determine stage boundaries, with weights dynamically adjusted based on task type. For ambiguous boundary cases, the segmenter retains data copies of overlapping areas, generating a stage segmentation scheme with confidence scores. Each stage records the environmental snapshot ID at startup and the agent's state fingerprint at termination, forming a traceable task execution context.

[0069] The dynamic mirror synchronization unit's anomaly handling mechanism comprises three levels of response: Level 1 response addresses instantaneous data anomalies by employing a sliding window mean filter to smooth sudden sensor value changes. Level 2 response addresses persistent data anomalies by initiating redundant sensor switching, such as switching to backup millimeter-wave radar point cloud data when the primary LiDAR fails. Level 3 response addresses system-level failures by freezing the current environmental mirror and issuing an alarm, while simultaneously recording a snapshot of the environmental state at the time of the failure. All anomaly events generate diagnostic reports, including the anomaly type, duration, and an assessment of the effectiveness of compensatory measures.

[0070] Example 2: See Figure 3 The spatiotemporal trajectory segmentation unit of the behavior feature reconstruction module receives a chain of interactive events with time-aligned markers generated by the interaction record analysis unit. This unit uses a sliding window segmentation algorithm to process the event chain, with a fixed decision-making period (e.g., 200 milliseconds) as the time window length. The segmentation process preserves the event integrity at the window boundaries: when an event crosses a window boundary, it is copied to an adjacent window and a boundary marker is added. Each generated behavior segment contains a complete event sequence, structured as <window start time, event list, environment snapshot ID>. For a picking task of a warehouse logistics agent, a typical behavior segment might contain an event sequence such as "robotic arm movement command → visual positioning feedback → grasping action execution → item weight detection," and is associated with an environmental snapshot of the shelf's 3D point cloud during that time period.

[0071] The 3D feature coupling unit initiates a processing flow for each behavior segment. First, it calls the spatial data from the environmental state parameter set and loads a 3D environmental topology mesh based on the environmental snapshot ID associated with the current behavior segment. This mesh uses an octree structure for encoding, with nodes storing spatial attributes (such as accessibility coefficient and illumination intensity) and edges storing topological connections (such as accessibility to adjacent areas). Next, it performs spatiotemporal correlation analysis: mapping the action command timestamps in the behavior segment to mesh spatial coordinates. For example, when the agent issues a command to "move to coordinates (X,Y,Z)," the unit calculates the mesh path between the command coordinates and the current agent position, recording the path node sequence and corresponding timestamps. The displacement response matrix is ​​generated by analyzing coordinate changes between adjacent actions. Rows represent time steps, columns represent spatial dimensions (X / Y / Z axes), and element values ​​record the displacement per unit time. The action selection probability distribution statistically analyzes the frequency of various actions within the window, such as the percentage of triggers for actions like "translation," "rotation," and "grab." The intent recognition vector is generated by parsing additional parameters of collaborative actions. For example, fields such as "expected load percentage" and "collaboration object ID" in multi-agent collaborative transport tasks are encoded as vector components.

[0072] The feature accumulation unit receives behavioral feature components from ten consecutive decision response windows (i.e., covering a 2-second operation period). The unit maintains a circular buffer to store the feature data from the ten most recent windows, discarding the oldest data when new window data arrives. Aggregation calculation employs a hierarchical processing method: for the displacement response matrix, the standard deviation of displacement along each spatial axis in the time dimension is calculated to form a three-dimensional response fluctuation vector; this vector is then subjected to time series differencing to derive the decision response delay gradient, with negative values ​​indicating an accelerating response trend and positive values ​​indicating an aggravating delay trend. Action space coverage calculation uses an information entropy model: the total number of triggers for each type of action within the ten windows is counted, and the Shannon entropy value of the action distribution is calculated; a higher entropy value indicates stronger action diversity. The collaborative intent fluctuation coefficient is obtained by analyzing the intent recognition vector sequence of the ten windows: the variance of the same intent component (e.g., "expected load ratio") on the time axis is calculated, and then a weighted average of the variances of all intent components is taken, with the weights determined by the importance of the collaborative task.

[0073] The policy optimization unit of the training decision model module receives the decision response delay gradient output by the behavior feature reconstruction module. This gradient data is input into the first policy layer of the agent evolution model: a temporal analysis layer composed of a long short-term memory network. The network divides the gradient sequence into 40 time-step input segments with a time step of 50 milliseconds. The response delay analysis algorithm is implemented in the hidden layer of the network: it identifies key inflection points in the gradient sequence (such as moments of sudden delay increases) through a gating mechanism and extracts feature patterns from the five time steps before and after the inflection point. The output layer generates policy response time optimization parameters, containing three sets of values: a decision frequency adjustment coefficient (suggesting an increase or decrease in the decision cycle), a sensor sampling rate optimization suggestion, and an environmental prediction model update flag. These parameters are encapsulated as control commands, such as "adjust the navigation decision cycle from 200 milliseconds to 180 milliseconds".

[0074] The behavior correction unit processes action space coverage data. This unit activates the second policy layer of the agent evolution model: an exploration efficiency evaluation layer based on a graph convolutional network. The network input consists of two parts: a scalar value of action space coverage and a set of action coverage blind zone coordinates. The blind zone coordinate set comes from the spatial analysis results of the feature accumulation unit, for example, marking spatial areas in a warehouse environment where the "top shelf access" action has not been performed. Action space exploration efficiency is calculated in a graph network: using an environmental topology mesh as the underlying graph structure, coverage as a node attribute, and blind zone coordinates as specially marked nodes. The network calculates the exploration value score of each region through a message passing mechanism, outputting the priority ranking of action coverage blind zone coordinates and the action repetition probability distribution. The latter is represented as a probability vector, such as "translational action repetition probability 0.25, rotation action repetition probability 0.18," used to suppress the agent's tendency to repeat behaviors.

[0075] The collaboration reinforcement unit receives the collaboration intent fluctuation coefficients and their component sequences. This data is input into the third strategy layer: a collaboration stability assessment layer composed of a multi-head attention mechanism. The layer structure includes three processing branches: a fluctuation trend analysis branch uses one-dimensional convolutional kernels to extract short-term fluctuation patterns in the coefficient sequences; a collaboration event correlation branch maps fluctuation coefficients to specific collaboration actions; and an environmental interference assessment branch analyzes the impact of sudden environmental events on collaboration. The intent stability assessment algorithm is implemented in the attention layer: it calculates the fluctuation correlation of different collaboration intent components and identifies key fluctuation sources. The output collaboration protocol update parameters include communication rule adjustments, responsibility allocation optimization, and conflict resolution mechanism updates.

[0076] In a typical industrial quality inspection application, an intelligent agent performs defect detection on parts on a conveyor belt. The behavior feature reconstruction module segments the detection action sequence according to the decision cycle. The 3D feature coupling unit constructs an environmental mesh including the conveyor belt, lighting system, and microscope camera, generating a "movement response matrix" (recording the camera position adjustment trajectory), an "action probability distribution" (statistically calculating the frequency of various detection actions), and a "cooperative intent vector" (encoding the cooperative parameters with the robotic arm). The feature accumulation unit calculates the response delay gradient, indicating a decrease in microscope focusing speed; the action coverage reflects insufficiently inspected bottom surfaces of parts; and the intent fluctuation coefficient indicates unstable lighting cooperation. The training decision model module outputs optimization schemes: adjusting the focusing algorithm response threshold (strategy optimization), adding bottom surface flip detection actions (behavior correction), and synchronizing the brightness curve of the lighting system (cooperative enhancement).

[0077] At the system implementation level, the 3D environment topology mesh employs dynamic memory management. The mesh infrastructure is pre-allocated during system initialization, and node attribute data is dynamically loaded according to behavioral fragment requirements. Spatiotemporal correlation analysis utilizes a GPU-accelerated ray casting algorithm to calculate the propagation path of action commands within the mesh. The circular buffer of the feature accumulation unit is designed as a double-precision floating-point array, and aggregation calculations are executed in parallel using CUDA kernel functions. The three-layer policy structure of the agent evolution model shares the underlying feature extractor, and layered training is achieved through a parameter masking mechanism. Model updates employ an online incremental learning strategy, with a full parameter snapshot synchronized to the cloud training cluster every morning.

[0078] The storage of behavioral feature tensors employs tensor slicing technology. Since the full tensor has dimensions as high as [time step × spatial dimension × action dimension × intent dimension], the system only stores non-zero slice indices and compressed feature data. The tensor access interface supports querying by time range and filtering by spatial region. Output parameters from the training decision model module are distributed via a message queue: policy optimization parameters are sent to the decision engine, behavior correction parameters update the action selector, and collaborative reinforcement parameters are synchronized to the communication middleware. All output parameters include a version timestamp, supporting backtracking analysis of training performance.

[0079] The anomaly handling mechanism includes a feature verification module. When an anomaly is detected in a behavioral feature component, the system initiates a data reconstruction process: tracing back from the original interaction event chain, it re-executes spatiotemporal trajectory segmentation and feature coupling. If reconstruction fails, the data for that time period is marked as invalid, and the current training cycle is skipped. Feature transmission employs CRC checksum and retransmission mechanisms to ensure that the behavioral feature tensor is delivered completely to the training decision module. A timeout monitoring system is implemented during model inference; if a single processing step exceeds 500 milliseconds, it automatically switches to a simplified model to ensure the continuity of real-time training.

[0080] Example 3: See Figure 4The environmental disturbance compensation module continuously monitors abrupt change events recorded in the environmental state parameter set. This module identifies abrupt changes by comparing the differences in environmental parameters across consecutive timestamps: when the rate of change of physical constraints or the distribution of dynamic obstacles exceeds a preset threshold, it is recorded as an environmental abrupt change event. Each event includes a change type identifier, a timestamp of the change, parameter values ​​before the change, parameter values ​​after the change, and the duration of the change. For example, in an outdoor navigation scenario, a sudden downpour causes the ground friction coefficient to drop from 0.8 to 0.3; the system records a friction coefficient abrupt change event, with the duration from the start to the end of the rainfall. The virtual mirror environment parameter drift is obtained by calculating the relative change in parameter values ​​before and after the change, and the degree of drift is quantified using the following formula:

[0081]

[0082] Where: Δ ∈ T represents the drift of environmental parameters. δ The duration of the mutation (in seconds) is n, where n is the number of environmental parameters affected, and w is the number of parameters affected. i P is the weight coefficient of the i-th parameter (set according to the importance of the parameter). i and P i ′ These represent the values ​​of the i-th parameter before and after the mutation, respectively. This formula comprehensively considers the magnitude of parameter change, duration, and importance of the parameter; for example, the friction coefficient is weighted at 0.7, and the light intensity is weighted at 0.3.

[0083] The environmental drift correction unit is based on Δ ∈ The 3D environment topology mesh is dynamically calibrated. This unit includes a drift detection sub-unit, a mesh reconstruction sub-unit, and a time-compensation sub-unit. The drift detection sub-unit analyzes environmental abrupt event records, extracting environmental attribute abrupt change values ​​and durations. The detection algorithm employs a sliding window difference technique, with the window size adaptively adjusted according to the environment type: a 5-second window is used for indoor environments, and a 20-second window is used for outdoor environments. The mesh reconstruction sub-unit receives abrupt change parameter values ​​and adjusts the topological connectivity weights of the 3D environment topology mesh. The connectivity weights, originally representing the difficulty of passage between areas, are recalculated based on the abrupt change parameters: for abrupt changes in friction coefficient, the connectivity weights of all ground areas are adjusted proportionally; for abrupt changes in illumination, the weights of the visual navigation area are adjusted. The weight adjustment formula is W. new =W old ×(1+αΔP), where W old and W newThese represent the weights before and after adjustment, ΔP is the rate of change of parameters, and α is the environmental sensitivity coefficient (0.6 for concrete pavement and 0.9 for grassland). A topology update matrix is ​​generated to store the weight changes, with the matrix dimension consistent with the number of grid nodes. A time-compensation sub-unit handles the impact of abrupt changes in duration, using an exponential decay model to correct the topology update matrix. Where M update It is an update matrix, T δ M represents the duration of the mutation, β is the decay coefficient (default value 0.01), and M... calibrated This is the calibrated matrix. Output the calibrated 3D environment topology mesh, updating the mesh node attributes to A. new =A old +M calibrated , where A represents the node attribute vector.

[0084] The policy drift suppression unit analyzes the correlation between the frequency of environmental mutations and policy stability. This relationship is established through historical data statistics: data on the past 100 environmental mutation events and their corresponding policy performance changes are collected to construct a mutation frequency-stability mapping table. For example, when the frequency of environmental mutations exceeds once per minute, the policy error rate increases by an average of 23%. Anti-interference enhancement processing strengthens the optimized policy parameter set based on this mapping table: for decision weight parameters, a noise tolerance boundary is added; for action selection thresholds, the safety margin is expanded; and for cooperation protocol parameters, a retry mechanism is added. The processing algorithm employs robust optimization techniques, finding the optimal parameters in the worst-case scenario by solving a min-max optimization problem. f(θ,δ), where θ represents the policy parameters, δ represents the environmental disturbance, and f is the performance function. A robust policy parameter set is generated, containing the main parameter values ​​and their corresponding disturbance tolerance ranges.

[0085] In an autonomous driving test scenario, the environmental disturbance compensation module detected a sudden fogging event, causing the visibility parameter to drop from 1000 meters to 50 meters, with the abrupt change lasting 180 seconds. The environmental parameter drift Δ was calculated. ∈ =0.85. Environmental drift correction unit calibration: The drift detection subunit identifies visibility abrupt changes (a decrease of 950 meters) and their duration (180 seconds); the mesh reconstruction subunit adjusts the connection weights of the road mesh, reducing the weight of visible areas by 30% and increasing the weight of invisible areas by 50%; the aging compensation subunit applies the attenuation factor e. -0.01×180 =0.165, generating a calibrated environmental mesh. The strategy drift suppression unit queries historical data and finds that the frequency of visibility abrupt changes is positively correlated with the path planning error rate. Therefore, a 20% margin is added to the safety distance parameter of the planning algorithm to generate a more robust navigation strategy.

[0086] In terms of system implementation, environmental mutation event records are stored using a circular buffer, retaining the most recent 1000 event records. Drift calculation uses fixed-point arithmetic to ensure real-time performance, with a weighting coefficient w. i The data is stored in a configurable file and supports dynamic adjustment. The topology update matrix is ​​stored in a sparse matrix format, recording only the data of nodes that have changed. The attenuation coefficient β of the time-compensation subunit supports online adjustment: it automatically increases when environmental changes are frequent (maximum 0.05) and decreases when changes are infrequent (minimum 0.005). The mapping table of the policy drift suppression unit is automatically updated every 24 hours, incorporating the latest training data to recalculate the correlation relationships.

[0087] The exception handling mechanism includes a drift verification procedure, when the calculated Δ ∈ When the value exceeds a reasonable range (e.g., greater than 2.0), the system initiates a review process: resampling environmental parameters to verify the authenticity of the mutation event. If the mutation is confirmed to be a pseudo-mutation caused by sensor anomaly, the event is discarded and the environmental parameters are reset. A rollback mechanism is implemented during the mesh calibration process. A mesh snapshot is saved before each calibration. If mesh connectivity conflicts occur after calibration (e.g., inaccessible areas become accessible), the system automatically reverts to the previous stable state. Stability checks are implemented during strategy parameter hardening. If robustness processing causes a performance degradation exceeding a threshold, a conservative hardening scheme (minimizing parameter adjustments) is adopted. Detailed logs are recorded for all calibration operations, including calibration time, parameter changes, and impact assessments, for subsequent analysis and optimization.

[0088] Example 4: See Figure 5 The training efficiency optimization module receives behavioral defect region identifiers from the training decision model module. These identifiers, using a spatial encoding format, divide the agent's operating environment into several cubic voxel units, each storing a defect type label and severity value. Defect types include decision delay defects (encoded DL), action coverage defects (encoded AC), and cooperative instability defects (encoded CI). After parsing these identifiers, the module generates a policy adjustment instruction set, encapsulated in JSON format, containing three main parts: a periodic adjustment suggestion for the decision frequency adjustment parameter in milliseconds; an action space expansion scheme including a list of coordinates to be explored and a sequence of action types; and a cooperative protocol reinforcement policy specifying communication frequency adjustment values ​​and task allocation weight modification suggestions.

[0089] The decision frequency adjustment unit processes decision response delay gradient data, presented in a time-series format, recording the rate of change in response delay within each decision cycle. The unit calculates the optimal decision response time threshold based on statistical analysis of historical delay data: first, abnormal fluctuations are removed; then, a moving average is calculated as a baseline; finally, a standard deviation is added to the baseline as a safety margin. For example, in a warehouse picking task, if the baseline delay is 180 milliseconds and the standard deviation is 25 milliseconds, the optimal threshold is set to 205 milliseconds. The calculation results are converted into decision frequency adjustment parameters. If the current decision cycle exceeds the threshold, it is recommended to shorten the cycle; otherwise, the cycle is extended. The parameter output format is {"decision_period_adjustment":+15}, indicating an increase of 15 milliseconds in the cycle.

[0090] The action space optimization unit receives action coverage blind zone coordinate data, represented in a local coordinate system. Each coordinate point contains location information and a blind zone type attribute. Blind zone types are divided into structural blind zones (not covered due to environmental structural limitations) and behavioral blind zones (not covered due to strategy defects). The unit constructs an action exploration path planning scheme in three stages: first, spatial clustering analysis is performed on the blind zone coordinates to identify blind zone clustering areas; then, an exploration path sequence is planned based on the clustering results; finally, action types and execution parameters are assigned to each path. The scheme output format includes a list of path nodes, a sequence of action instructions, and an expected coverage index.

[0091] Table 1: Results of blind zone cluster analysis.

[0092]

[0093] The blind zone analysis subunit processes the clustering data in the table above, extracting the spatial distribution characteristics of each cluster region, including calculating centroid coordinates, boundary convex hull, and volume density. The blind zone aggregation degree is obtained by analyzing the point set density distribution, using a kernel density estimation algorithm to calculate the concentration of the point set within a unit volume. The boundary curvature value is obtained by fitting a surface and calculating the average curvature, reflecting the degree of curvature of the blind zone boundary. These feature data are used to determine the parameters for subsequent path planning.

[0094] The path generation subunit determines the density of action exploration paths based on the clustering degree of blind spots. High-clustering areas (score > 0.8) use dense path planning with a node spacing of 0.5 meters; medium-clustering areas (score 0.5-0.8) use medium-density paths with a node spacing of 1 meter; and low-clustering areas (score < 0.5) use sparse paths with a node spacing of 2 meters. The path direction is determined based on the cluster shape: for elliptical clusters, paths are generated along the major axis; for irregular shapes, a grid-like path is used for coverage.

[0095] The direction control subunit adjusts the exploration direction angle based on the boundary curvature value. Regions with curvature values ​​greater than 0.4 use a tangential direction for exploration, creating a 15-degree angle between the exploration direction and the boundary; regions with curvature values ​​between 0.3 and 0.4 use a 30-degree angle; and regions with curvature values ​​less than 0.3 use a 45-degree angle. This angle setting ensures the exploration path effectively covers the boundary region while avoiding repeated traversal. The collaborative reinforcement execution unit processes the collaboration protocol update parameters. These parameters include communication frequency adjustment values, task allocation weight modification suggestions, and conflict resolution rule updates. The specific process of generating multi-agent communication rule adjustment instructions is as follows: first, the current collaboration protocol version is parsed to identify the clauses that need updating; then, the new protocol parameters are calculated based on the update parameters; finally, differential update instructions are generated. For example, for a multi-UAV collaborative patrol task, the instructions might include: "Increase the status broadcast frequency from 2Hz to 3Hz," "Adjust the area allocation weight coefficient from equal allocation to load-balanced allocation," and "Add an emergency avoidance priority rule."

[0096] In specific applications within industrial manufacturing scenarios, intelligent agents perform quality inspections on assembly lines. Behavioral defect area markers indicate motion coverage defects (AC type) at conveyor belt corners and collaborative instability defects (CI type) in the robotic arm collaboration area. The training efficiency optimization module generates a set of policy adjustment instructions: for decision frequency, it suggests adjusting the detection cycle from 200 milliseconds to 180 milliseconds; for motion coverage defects, it generates corner area exploration paths, including 5 detection paths and 12 key detection points; for collaborative instability defects, it suggests increasing the robotic arm status query frequency and implementing a timeout retry mechanism.

[0097] The motion space optimization unit processes the blind zone coordinates at the conveyor belt corner. The blind zone analysis subunit identifies the blind zone aggregation degree as 0.82 and the boundary curvature value as 0.38, classifying it as a high-aggregation, medium-curvature region. The path generation subunit generates dense paths with a node spacing of 0.6 meters, planning a total of 3 parallel detection paths. The direction control subunit sets the exploration direction at a 25-degree angle to the boundary to ensure coverage of the unique geometry at the corner. The final output motion exploration path planning scheme includes a path coordinate sequence, camera angle adjustment commands, and lighting parameter settings.

[0098] In terms of system implementation, the strategy adjustment instruction set adopts version control management, generating a unique version number for each adjustment and recording the adjustment time, impact scope, and expected effect indicators. Blind zone coordinate data uses an octree spatial index, supporting fast region queries and cluster analysis. The path planning algorithm is an improvement on the A* search algorithm, considering agent dynamics constraints and environmental obstacles. The cooperation protocol update adopts a hot deployment mechanism, allowing communication rules to be updated without interrupting system operation.

[0099] The exception handling mechanism includes a component for monitoring the effects of adjustments. When a performance decline is detected after a strategy adjustment, the system automatically reverts to the previous stable version. For path planning conflicts (such as intersecting paths generated by different units), a coordination and arbitration mechanism is initiated to replan the path. All adjustment commands are simulated and verified before execution, and are only deployed to the actual system after confirming the absence of conflicts through virtual environment testing.

[0100] Example 5: The model validation module initiates the data acquisition process within a preset validation period. This period is initially set to 30 minutes, and the system captures the target agent's operational data through a distributed probe network. The actual decision trajectory is recorded using a high-precision spatiotemporal log, storing the agent's action type, execution coordinates, and environmental state snapshot ID at 10-millisecond sampling intervals. Collaboration efficiency indicators are acquired through a dedicated monitoring channel, containing three types of data: communication indicators recording the latency and success rate of message interactions between agents; task coupling indicators quantifying the efficiency of task coordination in multi-agent collaboration; and resource contention indicators counting the number of conflicts in the use of shared resources. All collected data is appended with precise timestamps and stored in a dedicated validation database.

[0101] The policy error analysis unit loads predicted behavioral trajectory data, which is generated by the agent's evolutionary model at the start of the validation period. This data includes the expected action sequence and environmental response predictions. The unit performs deviation comparison processing: first, it aligns the timelines of the actual and predicted trajectories, using a dynamic time warping algorithm to compensate for temporal offsets; then, it calculates the state difference vector for each sampling point, including three dimensions: position deviation, action type difference, and environmental response error; finally, it aggregates the difference data from all sampling points to generate the policy execution error coefficient. This coefficient is calculated using a weighted average, with a weight of 0.5 for position deviation, 0.3 for action difference, and 0.2 for environmental response error. The output format is a multi-dimensional vector.

[0102] The model parameter update unit receives the policy execution error coefficient vector and maintains the evolution rate parameter matrix of the agent evolution model, which controls the adaptive learning rate of each layer of the model. The update process adopts a hierarchical adjustment mechanism: for the feature extraction layer, the feature weight update step size is adjusted according to the environmental response error component; for the decision layer, the policy gradient descent rate is corrected according to the action difference component; for the cooperation layer, the learning rate of the attention mechanism is optimized based on the communication index deviation. The adjustment algorithm uses constrained incremental updates, with each update not exceeding 15% of the current value. When generating the optimized agent evolution model, the system creates a new version model snapshot while retaining historical versions for rollback.

[0103] The dynamic sampling unit monitors the changing characteristics of collaboration performance indicators. This unit continuously analyzes the volatility of communication latency, the slope of change in task coupling, and the derivative of resource contention frequency. When the rate of change of an indicator exceeds a threshold, the verification cycle duration is adjusted: if the communication latency volatility is greater than 5% per second, the cycle is shortened to 15 minutes; if the slope of change in task coupling is less than 0.1, the cycle is extended to 45 minutes; if the derivative of resource contention frequency exceeds 0.2, the cycle is shortened to 10 minutes. The adjustment decision is determined through a voting mechanism; adjustment is executed if two of the three indicators meet the conditions. The new verification cycle takes effect immediately, and the system resets the data acquisition buffer.

[0104] The feedback fusion unit performs multi-source data correlation analysis. This unit receives the policy execution error coefficient vector and the cooperation protocol update parameter set, and establishes parameter mapping relationships: associating communication rule adjustment parameters with communication indicator errors, task allocation parameters with task coupling errors, and conflict resolution parameters with resource contention errors. The fusion process employs an adaptive weighted algorithm, allocating weights according to the error magnitude. When generating the model weight update amount for the fusion feedback, the system creates an update instruction packet containing a triplet of neural network layer name, weight index, and update magnitude.

[0105] The closed-loop optimization unit processes the model weight updates from the fusion feedback. This unit drives the mirror update frequency adjustment of the training environment mirror module. Specifically, it statistically analyzes the magnitude distribution of model weight updates. When more than 30% of the weight updates have a magnitude greater than 0.01, the mirror update frequency is increased from the standard 1Hz to 2Hz; when less than 10% of the weight updates have a magnitude greater than 0.01, the frequency is reduced to 0.5Hz. Frequency adjustment commands are sent to the training environment mirror module in real-time via a message queue, simultaneously updating the module's clock synchronization parameters. The system records the frequency adjustment history, forming a log linking update frequency and model performance.

[0106] In a port container scheduling scenario, the model validation module collects operational data from the AGV cluster within a 30-minute validation period. Actual decision trajectories record the AGV's travel path and loading / unloading operation times. Collaboration efficiency indicators include AGV-to-collision communication latency, yard task handover efficiency, and the number of shared crane conflicts. Strategy error analysis reveals that path planning deviations are concentrated in turning areas, generating error coefficients {"path offset": 0.15,"loading / unloading delay": 0.07,"avoidance communication error": 0.12}. Based on this, the model parameter update unit reduces the evolution rate of the path planning layer by 20% and increases the evolution rate of the communication layer by 15%. The dynamic sampling unit detects a continuous increase in the avoidance communication error rate and shortens the validation period to 18 minutes. The feedback fusion unit correlates the avoidance communication error with the distance parameter in the collaboration protocol, generating weight update amounts for the communication protocol layer. The closed-loop optimization unit, based on high-frequency weight updates, increases the environmental mirror synchronization frequency to 1.8Hz.

[0107] Actual decision trajectory data is stored using lossy compression to retain key action node data. Differential encoding is used for transmitting collaborative performance indicators to reduce bandwidth consumption. A dedicated hardware accelerator is deployed in the strategy error analysis unit to achieve real-time deviation calculation. A double-buffering mechanism is used in the model update process to ensure model service continuity. A sliding window filter is used to eliminate noise in the rate of change detection of the dynamic sampling unit. The parameter mapping table of the feedback fusion unit supports online editing, allowing experts to adjust association rules. A PID controller is used for smooth transition in the frequency control of closed-loop optimization. All operations are logged, including fields such as operator, timestamp, and parameter change details.

[0108] During the data acquisition phase, data validity checks are implemented, discarding data points that exceed physical laws. Compatibility checks are performed before model updates to prevent parameter conflicts. Safety boundaries are set during frequency adjustment, prohibiting deviations from the 0.2-3Hz range. If performance degradation is detected after three consecutive adjustments, the system automatically reverts to the previous stable configuration and issues a diagnostic request. All critical operations are guaranteed to be atomic through a transaction mechanism, ensuring recovery to a consistent state in the event of an interruption.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A mirror-based intelligent agent training system, characterized in that, include: The training environment mirror module is used to create a virtual mirror environment that is completely synchronized with the real physical environment. The virtual mirror environment includes a set of environmental state parameters, a sequence of agent interaction records, and a time series of performance indicators. The behavior feature reconstruction module is used to perform three-dimensional behavior trajectory parsing on the interaction record sequence of the agent to generate an agent behavior feature tensor. The agent behavior feature tensor includes decision response delay gradient, action space coverage and cooperative intention fluctuation coefficient. The training decision model module is used to call the pre-trained agent evolution model to perform policy space mapping processing on the agent's behavioral feature tensor, and generate the target agent's optimized policy parameter set and behavioral defect region identifier. The behavioral feature reconstruction module includes: The spatiotemporal trajectory segmentation unit is used to divide the interactive event chain into multiple behavioral segments according to the decision cycle, with each behavioral segment corresponding to a decision response window; A three-dimensional feature coupling unit is used to perform the following processing for each behavior segment: constructing a three-dimensional environmental topology mesh based on the set of environmental state parameters, performing spatiotemporal correlation analysis between the three-dimensional environmental topology mesh and the action command timestamp, and generating behavior feature components of the current decision response window, wherein the behavior feature components include a displacement response matrix, an action selection probability distribution, and an intent recognition vector. The feature accumulation unit aggregates and calculates the behavioral feature components of multiple consecutive decision response windows, and outputs the decision response delay gradient, action space coverage, and cooperative intention fluctuation coefficient. The decision response delay gradient reflects the changing trend of the agent's decision response time, the action space coverage represents the diversity of the agent's action choices, and the cooperative intention fluctuation coefficient measures the stability of the agent's cooperative intention.

2. The mirror-based intelligent agent training system according to claim 1, characterized in that, The training environment mirror module includes: The dynamic mirror synchronization unit updates the physical constraints and dynamic obstacle distribution data in the environmental state parameter set in real time based on the sensor data stream in the real physical environment. The interaction record analysis unit extracts the action command timestamps and environmental feedback delay data from the agent's interaction record sequence, and generates an interaction event chain containing time alignment markers; The performance metric tracking unit divides the performance metric time series into multiple metric subsequences according to task type, and each metric subsequence is associated with a task execution stage.

3. The mirror-based intelligent agent training system according to claim 2, characterized in that, The training decision model module includes: The strategy optimization unit is used to input the decision response delay gradient into the first strategy layer of the agent evolution model and generate strategy response time optimization parameters through a response delay analysis algorithm. The behavior correction unit is used to input the action space coverage into the second policy layer of the agent evolution model, perform action space exploration efficiency calculation, and generate action coverage blind zone coordinates and action repetition probability distribution. The cooperation reinforcement unit is used to input the cooperation intention fluctuation coefficient into the third strategy layer of the agent evolution model and generate cooperation protocol update parameters based on the intention stability evaluation algorithm.

4. The mirror-based intelligent agent training system according to claim 3, characterized in that, Also includes: The environmental disturbance compensation module extracts records of environmental abrupt events from the set of environmental state parameters and calculates the drift amount of the virtual mirror environment parameters; An environment drift correction unit performs dynamic calibration on the three-dimensional environment topology mesh based on the drift amount of the environmental parameters, and generates a calibrated three-dimensional environment topology mesh. The strategy drift suppression unit performs anti-interference enhancement processing on the optimized strategy parameter set based on the correlation between the frequency of environmental mutations and the stability of the strategy, thereby generating a more robust strategy parameter set.

5. The mirror-based intelligent agent training system according to claim 4, characterized in that, The environmental drift correction unit includes: A drift detection subunit is used to identify the environmental attribute mutation values ​​and mutation durations in the environmental mutation event records. The mesh reconstruction sub-unit is used to adjust the topological connection weights of the three-dimensional environment topological mesh according to the abrupt change value of the environmental attribute, and generate a topological structure update matrix; The time-compensation subunit is used to perform time-decay processing on the topology update matrix based on the duration of the mutation, and output the calibrated three-dimensional environmental topology mesh.

6. The mirror-based intelligent agent training system according to claim 5, characterized in that, Also includes: The training performance optimization module generates a strategy adjustment instruction set based on the behavioral defect area identifier. The strategy adjustment instruction set includes decision frequency adjustment parameters, action space expansion scheme, and cooperation protocol enhancement strategy. The decision frequency adjustment unit calculates the optimal decision response time threshold based on the decision response delay gradient. The action space optimization unit constructs an action exploration path planning scheme based on the coordinates of the action coverage blind zone. The collaborative enhancement execution unit generates multi-agent communication rule adjustment instructions based on the updated parameters of the collaborative protocol.

7. The mirror-based intelligent agent training system according to claim 6, characterized in that, The motion space optimization unit includes: The blind spot analysis subunit extracts the spatial distribution characteristics of the action-covered blind spot coordinates and calculates the blind spot aggregation degree and boundary curvature. The path generation subunit determines the action exploration path density based on the blind zone aggregation degree, and the action exploration path density is positively correlated with the blind zone aggregation degree. The direction control subunit adjusts the action exploration direction angle based on the boundary curvature, so that the exploration direction forms a preset angle with the blind zone boundary.

8. The mirror-based intelligent agent training system according to claim 1, characterized in that, Also includes: The model validation module is used to collect the actual decision-making trajectory and collaborative efficiency indicators of the target intelligent agent within a preset validation period. The strategy error analysis unit is used to compare the actual decision trajectory with the predicted behavior trajectory to generate a strategy execution error coefficient. The model parameter update unit is used to adjust the evolution rate parameter of the agent evolution model according to the strategy execution error coefficient, and generate an optimized agent evolution model.

9. The mirror-based intelligent agent training system according to claim 8, characterized in that, The model validation module includes: The dynamic sampling unit dynamically adjusts the verification cycle duration based on the rate of change of the collaborative performance index. The feedback fusion unit performs correlation analysis between the strategy execution error coefficient and the cooperation protocol update parameters to generate the model weight update amount of the fusion feedback. The closed-loop optimization unit drives the mirror update frequency of the training environment mirror module based on the model weight update amount of the fusion feedback.

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