67 results about "Strategy training" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of robot strategy training, financial science and technology, medical treatment and health and the like, and discloses a strategy optimization method and device based on an interaction track, equipment and a medium. The reinforcement learning agent interacts with the environment to generate an action and state sequence and record an interaction track, an optimal action strategy is generated based on a track optimization strategy, an original empirical data set is further generated, and a pre-training model is optimized through supervised learning to obtain a target strategy model. According to the method, a task-related high-quality trajectory is generated through reinforcement learning agent and environment interaction, original empirical data is extracted on this basis, and a pre-training strategy model is optimized in combination with a supervised learning mechanism, so that the sample utilization efficiency is effectively improved, and the generalization ability and execution robustness of the model under a multi-task condition are enhanced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a strategy generation method and device based on hierarchical reinforcement learning, equipment and a medium. And processing environment state information to generate a sub-target and a specific action, generating a strategy reward signal in combination with state change, carrying out joint training and updating on the dynamic causal graph and the hierarchical reinforcement learning model based on the strategy reward signal, and generating an optimized action strategy. The state evolution relation is modeled by constructing the dynamic causal graph, so that the reinforcement learning can obtain causal understanding of the state change trend, decomposition and optimization of sub-targets and actions are realized in combination with a layered reinforcement learning architecture, the response precision and generalization ability of the action strategy in a complex environment are improved, and the method is suitable for application and popularization. Therefore, the task completion stability and the convergence efficiency of strategy training are improved.

The invention discloses an optimization method and device for improving a near-end strategy based on a language model and electronic equipment, and the method comprises the steps: determining a plurality of answers corresponding to question data, and determining a first probability value of a single vocabulary in each answer based on the plurality of answers; determining a second probability value of a single vocabulary in each answer based on a reference model in the language model, and determining a KL divergence based on the first probability value and the second probability value; determining an instant reward for each answer based on a reward model in the language model and the target reward signal; and determining a target loss function based on the question data, the answer, the first probability value, the immediate reward and the KL divergence to update a policy model of the language model based on the target loss function. According to the method provided by the invention, the memory occupation problem in the near-end strategy training process can be relieved, and the memory usage is reduced, so that the concurrency is improved, and the answer generation and training process is accelerated.

The invention is applicable to the technical field of robot motion control, and provides a robot skill learning method, which comprises the following steps of: generating an auxiliary reward component function by utilizing a large language model according to learning prompt information, constructing a multi-branch value network according to a reward component function set formed by a plurality of reward component functions and auxiliary reward component functions in the learning prompt information, and performing iterative strategy training on the strategy network by using the multi-branch value network based on the weight coefficient of each value branch until the target function of the strategy network is converged, so as to obtain the target function of the strategy network. A strategy network is used for guiding robot skill learning, so that the intelligent decision-making capability of a large language model and fine-grained feedback of a multi-branch value network are combined, the strategy learning efficiency and the overall performance in reinforcement learning are improved, it is ensured that different skill obtaining requirements can be met in the strategy optimization process, and the robot skill learning efficiency is improved. And different environments and complex robot tasks can be handled, and the accuracy, precision and efficiency of task execution are improved.

The invention relates to the technical field of artificial intelligence and automatic driving, and discloses an automatic driving digital twin agent system based on reinforcement learning training, which comprises an agent library construction module, a simulation and configuration module, a distributed simulation execution module, a scheduling and learning module, a strategy training module and a simulation migration module. The method comprises the following steps: constructing a modular digital twin agent library, combining to generate an urban traffic simulation scene, executing large-scale parallel simulation on a distributed computing cluster, implementing dynamic task scheduling and course learning according to an agent learning progress, and training a reinforcement learning strategy and a value network based on simulation data. And verifying the strategy model and implementing migration deployment from simulation to reality. According to the method, the training efficiency can be improved, the diversity of simulation environments is enriched, and the sim-to-real migration difficulty is reduced, so that the robustness of the strategy model is improved.

The invention relates to an English course management system applied to English teaching, and the system comprises the steps: collecting multi-source English teaching data, carrying out the cleaning and feature extraction, and constructing a knowledge graph according to the extracted feature parameters; calculating curriculum configuration parameter factors by utilizing a feature filtering and weighting formula based on the knowledge graph; inputting the course configuration parameter factors into a deep reinforcement learning model for strategy training; outputting an optimal course content and difficulty configuration combination in the current teaching environment, and dynamically updating a teaching strategy; according to the scheme, a knowledge graph, multi-feature weighting and deep reinforcement learning combined mode is introduced, the complex feature relation in the English teaching process is accurately captured, the scheme can be evolved continuously through the self-adaptive optimization capability of reinforcement learning, and therefore the accuracy and flexibility of teaching resource allocation and course difficulty regulation and control are remarkably improved.

The invention discloses a strategy dynamic optimization system and method based on big data analysis and prediction, and relates to the technical field of data analysis and processing, and the system comprises a data collection layer which is used for building a data source; the data processing layer is used for performing data feature extraction after performing data cleaning on the data source; the extreme scene simulation layer is used for executing coping strategy training in an adversarial environment; the competition optimization layer is used for configuring a competition agent and updating an adaptive strategy set after adaptive evaluation of strategy quality, performing game optimization and generating a game optimization result; and the strategy dynamic optimization layer is used for receiving the game optimization result and establishing a dynamic response strategy. The technical problems that in the prior art, analysis deviation is caused by poor data quality, a static strategy is difficult to adapt to a complex and changeable market environment and cannot effectively cope with dynamic games of different competitive roles, and the strategy adaptability and flexibility are insufficient are solved, and the technical effect of improving the decision-making flexibility and adaptability of the fluctuating market environment is achieved.

The invention relates to a trading platform that shifts focus from market prediction to disciplined execution, capital management, and trading psychology. By disclosing the general market trend in advance, users are empowered to train and operate within a known directional framework. The system generates synthetic charts using directional random walks anchored at algorithmically or randomly selected convergence points from historical data. This recreates real trends while introducing stochastic variability between anchors. A simulated order book models supply-demand dynamics using Poisson-distributed flows, capturing sentiment, liquidity shifts, and large trades. Features include Accelerated Time Compression for fast-forwarded trade simulation and a Rewind Trade mechanism allowing entry at the start of validated candlesticks. The rewind logic applies to both futures and spot markets, including staking actions. Safeguards such as rewind limits, volume caps, and LP prioritization preserve fairness. The platform supports multi-timeframe execution and bridges strategic training with interactive trading, enhancing decision quality and behavioral discipline.

The invention discloses a training method, a resource allocation method, device and system and related equipment, and relates to the technical field of computers. Based on the system resource parameters and an acceleration packet set for the application, strategy training is executed, and the acceleration packet set comprises at least one acceleration packet; after strategy training is executed, a configuration strategy is output, the configuration strategy is used for conducting performance optimization on an application set, and the application set comprises at least one application. Therefore, the performance of at least one application included in the first application set can be optimized according to the configuration strategy determined by the system resource parameter and the at least one acceleration packet execution strategy training, so that after resource configuration is subsequently carried out on other applications with the same identifier or the same type based on the configuration strategy, the performance of the other applications can be optimized. And a relatively high level can also be achieved. Moreover, in the process of performing performance optimization on the application, internal data of the application does not need to be accessed, so that the security of the application can be effectively guaranteed.

The invention discloses an agent man-machine cooperation strategy training system, method and device and a medium. The system comprises a teammate strategy population generation module, a centralized training module and a distributed optimization module. The teammate strategy population generation module is used for determining an initial strategy population according to a preset population number, and performing co-evolution on the initial strategy population to determine a target teammate strategy population; the centralized training module is used for acquiring global trajectory information according to the target teammate strategy population and performing centralized training on a global context encoder according to the global trajectory information; and the distributed optimization module is used for determining individual track information from the global track information and optimizing the local context encoder according to the individual track information. Based on the technical scheme, the cooperation effect of the intelligent agent in the complex heterogeneous environment is remarkably improved, a control strategy capable of being matched with any multi-behavior style human teammates can be achieved, and then the cooperation effect of the intelligent agent in the complex heterogeneous environment is improved.

The invention discloses a digital twin model placement and resource allocation method based on action mask reinforcement learning, and solves the problems of low reinforcement learning efficiency, lack of modeling for edge resource competition and the like caused by unhooking of a deployment strategy and resource allocation and overlarge action space. According to the method, the model placement position selection and the computing power resource allocation process are uniformly modeled into the Markov decision process, and the action mask mechanism is introduced to dynamically constrain the action space, so that the strategy training efficiency and the effectiveness of the deployment decision are improved. According to the method, not only is the competition relation of a plurality of digital twin service requests under shared edge node resources modeled finely, but also a potential function-based reward function is constructed, and a strategy is guided to converge to a system time delay minimization target. Finally, collaborative optimization of digital twinborn model deployment and resource allocation in a dynamic environment is realized, the computing power resource utilization rate and the service quality are improved, and the high requirements of an industrial scene for real-time performance and intelligence are met.

The invention relates to a robot agent evaluation system for a lower limb exoskeleton and a gait strategy training method corresponding to an agent, and belongs to the technical field of rehabilitation medical auxiliary equipment. The device comprises a humanoid robot, an exoskeleton, an auxiliary device and a detection element, the exoskeleton is connected with the humanoid robot through the auxiliary device, a first auxiliary structure is provided with a groove, a second auxiliary structure is provided with a protrusion, the groove is connected with the humanoid robot in a clamped mode, and the protrusion is detachably connected with the humanoid robot; the detection elements are installed on the soles of the humanoid robot. The gait of the humanoid robot is adjusted by using a gait strategy training method, so that the humanoid robot can simulate the walking postures of different healthy people or patients with different hemiplegia levels. By wearing the exoskeleton on the device and deploying different walking aid strategies, related data such as angle, torque and power change of each joint, total energy consumption and gait symmetry of the humanoid robot under different walking conditions are obtained, and an exoskeleton developer is assisted to better test and optimize an exoskeleton product.

The invention discloses a multi-energy micro-grid real-time regulation and control method fusing topology perception and meta-learning self-adaption, and the method comprises the steps: inputting electric-heat-gas multi-source heterogeneous data obtained in real time into a pre-trained lightweight student model, and generating a prediction multi-energy flow cooperative regulation and control strategy. When the model is trained, firstly, a corresponding multi-energy micro-grid graph structure is constructed based on the network topology of the multi-energy micro-grid, multi-source heterogeneous data used as training data is combined and input to a teacher model constructed based on a graph neural network, and a corresponding predictive multi-energy flow cooperative regulation strategy is output; calculating a loss function based on the strategy and a corresponding optimal multi-energy-flow cooperative control strategy, and optimizing network parameters of the teacher model; and finally, migrating the knowledge of the trained multi-teacher network to the lightweight student model through a method of combining knowledge distillation, meta learning and reinforcement learning to obtain a trained lightweight student model. According to the invention, the problems of multi-target optimization, real-time regulation and control and cross-scene adaptation of the multi-energy micro-grid are solved.

The invention discloses an air combat combat strategy training method based on a PPO algorithm and a Transform-XL framework, and the method comprises the steps: enabling an intelligent agent to have more advantages in the processing of long-time sequence data through the segmentation memory and the modeling of a long-range dependency relationship in the Gated Transform-XL framework, and improving the capturing capability of the intelligent agent for the long-time dependency relationship; the intelligent agent can better adapt to the dynamically changing battlefield environment, and the applicability and robustness of the strategy are improved.

The invention discloses a vehicle network interactive regulation and control method and system based on multi-task off-line element reinforcement learning, and the method comprises the steps: constructing a regulation and control strategy training mechanism based on an off-line element reinforcement learning framework, introducing a element network for task recognition, and obtaining a regulation and control strategy through a semantic-physical fusion mechanism enhanced by a large language model. And combined modeling of historical moving trajectory data and expert scheduling text knowledge is realized. And the element network generation task specific embedding is used for guiding a downstream strategy network to quickly adapt to a dynamic regulation and control task so as to formulate a context-aware regulation and control strategy. According to the method, the bottleneck of a traditional reinforcement learning method in the aspects of task mobility and generalization ability is effectively broken through, and the adaptation efficiency of a strategy network to diversified EV distribution and regulation targets is remarkably improved.

The invention relates to the technical field of bidding and tendering, and discloses a bidding and tendering file deep understanding and multi-rule review management system based on a large model, and the system comprises an environment state representation module which is used for encoding a bidding and tendering file into a structured state vector; the multi-agent decision-making module comprises a plurality of professional agents and is used for executing a review action according to the state vector; the dynamic risk transmission module is used for generating a risk broadcast vector when a professional agent identifies the risk, and adjusting subsequent actions of other agents through a cross-agent attention mechanism; the strategy training module is used for training the strategy of the multi-agent decision-making module by utilizing an expert review track through a generative adversarial imitation learning mode; and the report generation module is used for outputting a dynamic review path report. According to the method, the dynamic optimization of the review path is realized by simulating an expert strategy, the risk collaborative review across professional fields is realized through a risk transmission mechanism, and the traceability of the review process is ensured at the same time.

The invention provides a multi-spacecraft orbit game method based on MAPPO. The method comprises the following steps: step 1, establishing an orbit pursuit game scene model; 2, designing reward functions of the spacecrafts of the two parties in the pursuit game; 3, remodeling a pursuit game reward function; 4, training a pursuit game strategy network; according to the invention, aiming at an orbit game scene model, a guide reward function is designed, a strategy training network is perfected, and the problem that a strategy network designed by a traditional reward function is not easy to converge is solved. According to the method, the MAPPO algorithm is combined to complete pursuit game intelligent control strategy network training, the trained network is utilized, the track pursuit game problem under the constraint conditions of considering track dynamics, maneuvering modes and capabilities, fuel consumption and the like is solved, and intelligent control over the pursuit game is achieved.

The invention relates to the field of data processing, and relates to an intelligent optimization system for enhancing programmed advertisement bidding based on trajectory data. Comprising a simulation bidding module used for performing data enhancement on original advertisement bidding data so as to obtain enhanced advertisement bidding data; the track data generation module is used for arranging the enhanced advertisement bidding data into track data capable of being learned by an IQL model; the IQLTrainer module is used for generating training parameters of an IQL model, constructing an IQL model instance in combination with the trajectory data, training the IQL model, and storing optimal model parameters after training is finished; the IQL model is used for acquiring a bidding strategy according to the current state of single real-time bidding; the training parameters comprise a budget proportion, an advertiser ID, a bidding model name and an operation parameter index. By adopting the system, the convergence efficiency of a bidding algorithm can be improved, and the noise of a training track is reduced.

The invention discloses an operator short message task distribution method and system based on machine learning, and particularly relates to the technical field of short message distribution, and the method comprises the steps: collecting historical strategy training data and historical score training data in advance; training a first machine learning model for executing prediction based on the historical strategy training data; training a second machine learning model used for executing matching based on the historical score training data; k short message matching tasks sent by the user side at the S moment are obtained, an initial operator matched with the K short message matching tasks is obtained, and a strategy adjustment scheme is obtained to distribute short messages; according to the method, the historical strategy training data and the score training data are combined, the strategy adjustment progress is dynamically generated and the initial matching operator set is initially matched, the change in the short message task allocation process can be responded in real time, a precise allocation strategy is provided for different operator resource states and task requirements, and the task adaptation degree and the execution efficiency are improved.

The invention discloses a video big language model security assessment and defense method, system, device and medium, which are corresponding schemes, and in the scheme, a constructed video risk assessment data set is large in scale, comprehensive in coverage, hierarchical and multi-language in design, so that the data set has stronger risk generalization ability, and the security assessment and defense of the video big language model is realized. And the performance and security of the model in an actual deployment environment can be effectively improved. In the two-stage post-training strategy, the method in the first stage is a context sensing type risk detection token fine adjustment method, and the risk signal sensing capacity of the model during complex input processing can be remarkably improved; the method in the second stage is a security reward-driven dynamic strategy optimization method, and the stability and generalization of strategy training can be improved. The finally obtained optimized video big language model can efficiently realize risk detection and safety response, and is especially suitable for scenes such as real-time content auditing, online education question answering, government affair video monitoring and the like.

The application relates to a multi-agent reinforcement learning training method and system, which comprises the following steps: a local strategy training stage, in which an agent makes an action by using local observation information; a global strategy training stage, in which a global strategy of the agent uses the local strategy as an action module for interacting with an environment, the global strategy takes global state of the environment as input, encodes the global information in a hidden space, and uses a neural network to fit the global state by using local observation information of all agents; the local strategy makes a suitable action in the environment according to the local observation information and the output of the global strategy; and a local strategy optimization stage, in which the global strategy, the local strategy and a fitting model obtained in the previous two stages are used to optimize the existing local strategy, so that an agent with better effect is finally obtained. The application can improve the speed and accuracy of multi-agent reinforcement learning training.

The invention provides a costume design system and method based on artificial intelligence, and relates to the field of artificial intelligence, and the method comprises the steps: collecting multi-dimensional design data through a cloud collaborative architecture of costume design resources; performing dynamic hierarchical allocation on the multi-dimensional design data to obtain a hierarchical allocation result, and performing sparse processing on the multi-dimensional design data to obtain a sparse model of the costume design resources; performing strategy training based on deep reinforcement learning on the sparse model according to a hierarchical distribution result and historical collaborative data to obtain a sharing strategy and an optimized migration parameter; constructing a knowledge migration component of the cloud collaborative architecture based on the sharing strategy and the optimized migration parameters; knowledge migration optimization is carried out on the collaborative process of the costume design resources based on the knowledge migration component through the cloud collaborative architecture, the costume shared resources for intelligent generalization are generated, dynamic hierarchical distribution and knowledge migration optimization can be carried out on the costume design resources, and intelligent generalization sharing of the costume design resources is achieved.

The invention discloses a coping strategy digital training method and device, a terminal and a storage medium. The method comprises the following steps: receiving individual basic information and environment state information input by a user; performing demand clarification grading evaluation on the user; performing thinking feature classification analysis according to the user feature data to judge a training type; setting individual coping strategy training parameters of each parameter component containing the multi-dimensional scene factor indexes; generating a coping strategy training plan; executing multi-mode man-machine interaction preset scene coping strategy loop training of the corresponding project; calculating and evaluating an adaptation index; and outputting a training report. The invention provides a self-service, systematized, structured and continuously optimized psychological adaptive ability training scheme, and the defects of the existing psychological service in the aspects of coverage, systematicness, self-adaptability and accessibility are effectively overcome.

The invention provides a dynamic experience priority management method and system for task type dialogue strategy optimization. The method comprises the steps that information such as states, actions and rewards is obtained in the dialogue interaction process; constructing an experience evaluation mechanism based on the main Q network and the target Q network, performing conjoint analysis on the current learning income and the historical learning trend of each experience, and quantifying the empirical value state; dividing experiences into four types of normal forms, namely an unmastered normal form, a forgotten normal form, a stable normal form and a noise normal form; a sampling priority is generated in a self-adaptive mode according to the empirical state, and the sampling proportion is dynamically adjusted through threshold value mapping, a piecewise function and a hierarchical weight strategy; a hierarchical tree structure is employed to implement weighted sampling for strategy training. According to the method, the influence of noise samples can be inhibited, forgotten high-value experience can be activated, the effective sample proportion can be improved, dialogue strategy convergence can be accelerated, and the method is suitable for a multi-domain and multi-task complex task type dialogue system and an experience playback-based reinforcement learning scene.

The invention discloses a preference reinforcement learning-based body-equipped agent strategy training method and system, and the method comprises the steps: obtaining and constructing a multi-source non-stationary environment preference data set: collecting historical interaction trajectories from different bottom-layer dynamic environments, and generating trajectory fragment pairs and corresponding human preference labels; carrying out environment dynamic predictor training, and enabling a calculation control unit to grasp dynamic evolution and dynamic change rules of the body-equipped intelligent body in different physical environments in advance; executing decoupling joint optimization based on conditional mutual information and a regret model; extracting a pure task award decoder which is absolutely immune to any dynamic physical disturbance; security underlying action strategy distillation is executed based on expectation regression, and a final action strategy for controlling physical equipment is safely generated by using pure rewards; and after the strategy network is trained and converged, the processor compiles the strategy network into an executable instruction, and directly deploys the executable instruction to a control unit of the target physical equipment to execute intelligent action control.

The application discloses a kind of intelligent cooperative confrontation strategy generation methods based on heterogeneous unmanned aerial vehicle group, belong to multi-agent deep reinforcement learning field.The application includes the following steps: constructing cooperative confrontation scene, constructing blue side confrontation rule strategy model, constructing red side heterogeneous multi-agent reinforcement learning model, intelligent strategy training and generation.The application is directed to intelligent cooperative confrontation strategy generation method problem, abstracts electromagnetic confrontation and fire confrontation mechanism, and innovatively constructs heterogeneous multi-agent reinforcement learning model, and realizes intelligent confrontation strategy generation by data training.

Training and / or utilizing a hierarchical reinforcement learning (HRL) model for robotic control. The HRL model can include at least a higher-level policy model and a lower-level policy model. Some implementations relate to technique(s) that enable more efficient off-policy training to be utilized in training of the higher-level policy model and / or the lower-level policy model. Some of those implementations utilize off-policy correction, which re-labels higher-level actions of experience data, generated in the past utilizing a previously trained version of the HRL model, with modified higher-level actions. The modified higher-level actions are then utilized to off-policy train the higher-level policy model. This can enable effective off-policy training despite the lower-level policy model being a different version at training time (relative to the version when the experience data was collected).

The present application relates to a kind of robust confrontation training framework for multi-agent reinforcement learning energy system, comprising: constructing an adversarial agent to generate adversarial attack, and modeling as adversarial partially observable stochastic game system;Fixed pre-trained victim multi-agent strategy, train an optimal deterministic confrontation strategy to produce bounded disturbance;Fixed optimal confrontation attack strategy, improve the robustness of victim strategy under optimal attacker through adversarial training.The beneficial effects of the present application are: the present application models adversarial attack as an attack adversary based on single-agent reinforcement learning, and learns the strongest attack strategy considering attack constraints.Mathematically, the problem is constructed as a confrontation Markov game, and the performance of the integrated energy management system based on multi-agent reinforcement learning is improved through robust confrontation training.

The application discloses a video large language model security evaluation and defense method, system, device and medium, which are corresponding solutions, wherein the constructed video risk evaluation dataset is large in scale and comprehensive in coverage, hierarchical and multilingual design, so that the dataset has stronger risk generalization capability, and the performance and security of the model in the actual deployment environment can be effectively improved. In the two-stage post-training strategy, the method of the first stage is a context-aware risk detection token fine-tuning method, which can significantly improve the risk signal perception ability of the model when processing complex inputs; the method of the second stage is a security reward-driven dynamic strategy optimization method, which can improve the stability and generalization of strategy training. The optimized video large language model obtained finally can efficiently realize risk detection and security response, and is especially suitable for scenes such as real-time content review, online education and question answering, government video monitoring and the like.

The application discloses a kind of training system and method of multi-agent cooperation communication strategy based on teammate perception, with the help of teammate modeling method, generate customized, sparse message for teammates in the process of interacting with teammates. Through multi-agent reinforcement learning communication method, the agent can learn efficient cooperation communication strategy that can resist interference under the condition of limited bandwidth, and the agent cooperation communication strategy obtained by training can enable the agent to select reasonable cooperation action according to the observation information obtained by itself, and send information to teammates at appropriate time to promote cooperation, or cooperate according to the teammate information received. The application realizes close and effective cooperation through communication between a small number of nodes in a limited bandwidth actual scene, and can ensure the anti-interference ability in the process of multi-agent cooperation communication.