Multi-Agent Reward Shaping for Cooperative Reinforcement Learning
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Solution Overview
Problem
In multi-agent reinforcement learning, agents may interfere with cooperative actions due to uneven reward distribution, leading to suboptimal learning outcomes.
Innovation Solution
A learning system that calculates evaluation values for cooperative actions with and without a target agent, using these values to determine a reward penalty or tax, ensuring appropriate reward distribution for cooperative actions among multiple agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If each agent performs learning to maximize its own reward, then individual agent performance is improved, but cooperative action learning is interfered with
Solution Approach 1:
The patent introduces an intermediate evaluation mechanism that assesses the impact of each agent on the cooperative action before distributing rewards. This intermediary evaluation layer prevents direct conflict between individual reward maximization and cooperative action learning by mediating the reward distribution process through impact assessment.
Solution Approach 2:
The patent changes the reward distribution parameters by introducing impact evaluation results as a modifying factor. Instead of fixed reward distribution rules, the system dynamically adjusts reward amounts based on each agent's actual contribution to cooperative actions, thereby aligning individual incentives with collective goals.
2Ease of operation
If reward is distributed based on individual agent performance, then individual motivation is improved, but cooperative action coordination deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the impact evaluation results are fed back into the reward distribution process. This feedback loop ensures that agents receive rewards that reflect both their individual performance and their contribution to cooperative actions, thereby maintaining motivation while improving coordination stability.
Solution Approach 2:
The patent makes the reward distribution system dynamic by adjusting reward amounts based on real-time impact evaluation results. This dynamic adjustment allows the system to adapt to changing cooperative action requirements while maintaining individual agent motivation through performance-based rewards.
Data Source
AI summary
A learning system for performing reinforcement learning of a cooperative action by agents includes the agents; and a reward granting unit configured to grant a reward. The reward granting unit performs a step of, in the presence of a target agent to which the reward is to be granted, calculating an evaluation value relating to a cooperative action of other agents as a first evaluation value; a step of, in the absence of the target agent, calculating an evaluation value relating to a cooperative action of the other agents as a second evaluation value; and a step of calculating a difference between the first and second evaluation values as a penalty of the target agent and calculating the reward to be granted to the target agent based on the penalty. The target agent performs learning of the decision-making model based on the reward granted.