Recursive Reinforcement Control for Multi-Actor Agent Actions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous devices face challenges in determining actions that prevent deviations from specified operations, especially in complex environments with multiple actors and objects.
Innovation Solution
A processor implements a recursive reinforcement learning model to determine actions for an agent based on predicted actions of actors in its environment, using first, second, and third-order reinforcement learning models to optimize rewards and penalties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous devices use computing systems to determine actions that prevent deviations from specified operations, then operational reliability is improved, but device complexity increases
Solution Approach 1:
The computing system is divided into multiple specialized modules: sensor data acquisition module, actor identification module, reinforcement learning model module, and action determination module. Each module handles a specific aspect of the autonomous decision-making process, improving reliability through specialized processing while managing complexity through functional decomposition.
Solution Approach 2:
A recursive reinforcement learning model acts as an intermediary between raw sensor data and final action determination. The model processes predicted actions of actors in the environment and translates them into safe actions for the autonomous device, serving as a mediator that enhances operational reliability while abstracting away the complexity of real-time decision-making.
2Reliability
If the computing system determines actions to prevent deviations from specified operations, then safety is improved, but loss of time in decision-making increases
Solution Approach 1:
The system performs preliminary identification of actors in the environment and pre-computes their predicted actions using the reinforcement learning model before actual interactions occur. This advance preparation allows the autonomous device to make safer decisions without excessive delay during critical moments of operation.
Solution Approach 2:
The recursive reinforcement learning model continuously receives feedback from sensor data about the current environment state and adjusts predicted actions of actors accordingly. This feedback loop enables the system to maintain safety by adapting to changing conditions while optimizing decision-making time through iterative refinement rather than exhaustive computation.
3Measurement precision
If the system uses recursive reinforcement learning models to predict actions of multiple actors, then measurement precision of actor behavior is improved, but device complexity increases
Solution Approach 1:
The system implements nested reinforcement learning models where first-order models predict basic actor actions, second-order models predict how actors respond to the autonomous device's actions, and third-order models predict higher-level strategic behavior. This nested structure improves prediction accuracy by capturing multiple levels of actor reasoning while managing complexity through hierarchical organization of models.
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.