Recursive Reinforcement Models for Predictive Agent Action Control
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Solution Overview
Problem
Autonomous devices face challenges in determining actions that prevent deviations from specified operations, particularly in navigating and interacting with dynamic environments, where existing technologies struggle to accurately predict and respond to the actions of multiple actors and agents.
Innovation Solution
Implementing a recursive reinforcement learning model that determines actions for an agent based on predicted actions of actors in its environment, using machine learning models to maximize rewards and minimize penalties, and continuously updating models with data from sensors and servers to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous devices use computing systems to determine actions that prevent deviation from specified operations, then operational reliability is improved, but the device complexity increases due to the need for sophisticated prediction and decision-making models
Solution Approach 1:
The computing system is divided into specialized modules: a reinforcement learning model for predicting actor actions, a separate decision-making module for determining agent actions, and distinct training and deployment components. This segmentation allows each module to be optimized independently while working together to ensure operational reliability.
Solution Approach 2:
The system performs preliminary action by training the reinforcement learning model offline with extensive actor action data before actual deployment. The model learns and stores prediction patterns in advance, enabling rapid real-time predictions during autonomous operation without requiring complex runtime computations.
2Measurement precision
If the computing system uses sophisticated models to predict actions of multiple actors and agents, then measurement precision of environmental dynamics is improved, but the use of energy increases due to complex computations
Solution Approach 1:
The reinforcement learning model is trained offline using extensive actor action data to pre-compute and store prediction patterns. During real-time autonomous operation, the pre-trained model performs rapid predictions without requiring intensive computations, thereby reducing energy consumption while maintaining high prediction accuracy.
Solution Approach 2:
The system replaces traditional physics-based simulation and complex real-time calculation mechanisms with a data-driven reinforcement learning model. This substitution leverages pattern recognition from training data rather than computationally intensive mechanical simulations, reducing energy requirements while improving prediction accuracy.
3Adaptability or versatility
If the system continuously updates models with data from sensors and servers, then adaptability to dynamic environments is improved, but the loss of time for data processing increases
Solution Approach 1:
The system implements periodic action by updating the reinforcement learning model at scheduled intervals rather than continuously. Training occurs periodically using accumulated sensor data and server information, allowing the system to maintain adaptability while avoiding constant data processing that would cause time losses during autonomous operation.
Solution Approach 2:
The system performs preliminary data processing and model updates during idle periods or between operational tasks. By preparing updated models in advance before they are needed, the system minimizes data processing time during critical autonomous operation phases while still maintaining environmental adaptability.
Data Source
AI summary
According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to access data about an environment of an agent, identify an actor in the environment, and access candidate models, in which each of the candidate models may predict a certain action of the identified actor. The instructions may also cause the processor to apply a selected candidate model of the accessed candidate models on the accessed data to determine a predicted action of the identified actor and may implement a recursive reinforcement learning model using the predicted action of the identified actor to determine an action that the agent is to perform. The instructions may further cause the processor to cause the agent to perform the determined action.