Agent Decision-Making With Episodic Future Behavior Prediction
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Solution Overview
Problem
In multi-agent scenarios, existing reinforcement learning techniques fail to consider future behaviors of surrounding agents, leading to potential safety issues and suboptimal reward acquisition.
Innovation Solution
A decision-making method and apparatus that collect and analyze observation and behavior information of surrounding agents using maximum likelihood and gradient descent methods to predict their future behaviors, allowing the main agent to determine optimal actions considering future environment information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If reinforcement learning determines behavior based on single time point information, then decision-making speed is improved, but safety deteriorates due to ignoring future behaviors of surrounding agents
Solution Approach 1:
The system performs preliminary actions by predicting future behaviors of surrounding agents before making decisions. The behavior prediction module forecasts future states of surrounding agents, and this predicted information is incorporated into the current decision-making process, allowing the main agent to anticipate and prepare for future situations rather than reacting to past events alone.
2Reliability
If reinforcement learning considers future behavior prediction of surrounding agents, then safety is improved, but computational complexity increases
Solution Approach 1:
The system segments the complex decision-making process into distinct functional modules: observation information collection module, behavior prediction module, and decision-making module. Each module handles a specific aspect of the problem independently, which simplifies the overall computational complexity while maintaining safety through comprehensive future behavior consideration.
3Productivity
If deep reinforcement learning is applied to multi-agent situations, then reward acquisition is improved, but the need for complex behavioral prediction increases device complexity
Solution Approach 1:
The system extracts and focuses on predicting only the behaviors of surrounding agents that are relevant to the main agent's decision-making. The behavior prediction module selectively forecasts future states of surrounding agents based on their observed patterns and character coefficients, rather than attempting to model all possible interactions in the multi-agent system, thereby reducing computational complexity while maintaining reward acquisition capability.
Data Source
AI summary
The present disclosure relates to an apparatus and a method for deciding a behavior of an agent, and more particularly, to an apparatus and a method for deciding a behavior of a single agent using an episodic future thinking mechanism. The decision-making method according to an exemplary embodiment of the present disclosure includes collecting observation information and behavior information of a surrounding agent, by a first information collecting unit; inferring a character coefficient of a surrounding agent using data of the first information collecting unit, by a character inferring unit, collecting observation information of a main agent and the surrounding agent at a first time point, by a second information collecting unit; predicting a behavior of the surrounding agent based on the observation information and the character coefficient of the surrounding agent, by a behavior predicting unit, inferring expected observation information of the environment state and the surrounding agent at a second time point corresponding to the behavior prediction result of the surrounding agent, by a state inferring unit; and deciding a behavior of the main agent at the first time point based on the expected observation information of the environment state and the surrounding agent at a second time point, by a decision-making unit.