Curiosity-Driven Multi-Agent Reinforcement Learning with Replay Clustering
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Solution Overview
Problem
Multi-agent reinforcement learning systems struggle to effectively calculate the influence of other agents' actions and reflect optimal policies, leading to limited exploration and slow convergence due to small sample selection from replay buffers, especially in complex environments.
Innovation Solution
The method involves calculating the similarity between agents' policies using conditional mutual information and KL divergence, clustering replay buffer data based on policy similarities, and prioritizing samples to enhance learning by considering the influence of other agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-agent reinforcement learning selects only a small number of samples from replay buffer, then training efficiency is improved, but agents cannot make various attempts and find limited approaches when solving complex problems
Solution Approach 1:
The replay buffer is segmented into multiple clusters based on policy similarity using hierarchical agglomerative clustering. This segmentation allows diverse experiences to be organized into distinct groups, enabling the system to sample from multiple clusters and thus maintain both training efficiency and exploration capability by selecting samples across different segments rather than treating the buffer as a single homogeneous pool.
Solution Approach 2:
The patent introduces a new dimension of policy similarity measurement using conditional mutual information and KL divergence to cluster experiences. By adding this dimensional criterion for organizing replay buffer samples, the system can efficiently navigate diverse experience spaces while maintaining controlled sample sizes, thus resolving the contradiction between efficiency and exploration.
2Device complexity
If multi-agent reinforcement learning calculates influence of other agents relying only on neural networks, then system complexity is reduced, but optimal policy according to actions of other agent cannot be reflected in the learning
Solution Approach 1:
The patent introduces conditional mutual information and KL divergence as intermediary metrics to measure policy similarity and influence between agents. These mathematical tools act as mediators that quantify the influence of other agents' actions more precisely than neural networks alone, enabling accurate reflection of optimal policies while maintaining manageable system complexity through well-established information-theoretic measures.
Solution Approach 2:
The patent replaces or supplements the neural network-based influence calculation with information-theoretic measures (conditional mutual information and KL divergence). This substitution provides a more precise and interpretable method for calculating policy influence, transitioning from a black-box neural network approach to a mathematically rigorous measurement framework.
3Quantity of substance
If replay buffer size is increased to store more experience data, then more diverse experiences are available, but training efficiency decreases due to larger data processing requirements
Solution Approach 1:
The large replay buffer is segmented into multiple clusters based on policy similarity. This segmentation allows the system to efficiently manage large volumes of experience data by organizing them into manageable groups, enabling selective sampling from relevant clusters rather than processing the entire buffer, thus maintaining training efficiency while accommodating diverse experiences.
Solution Approach 2:
The patent performs preliminary clustering of the replay buffer before the sampling phase. By pre-organizing experiences into clusters based on policy similarity, the system prepares the data structure in advance, enabling efficient sampling operations that maintain training efficiency while utilizing the full diversity of stored experiences.
Data Source
AI summary
Provided is a method for exploration based on curiosity and prioritization of experience data in multi-agent reinforcement learning, the method including the steps of: calculating a similarity between a policy of a first agent and a policy of a second agent and computing a final reward using the similarity; and performing clustering on a replay buffer using a result of calculating the similarity between the policy of the first agent and the policy of the second agent and performing sampling on data in the cluster.