Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2412.14779v1
• Date: Thu, 19 Dec 2024 12:05:13 GMT
• Title: Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
• Authors: Aditya Kapoor, Sushant Swamy, Kale-ab Tessera, Mayank Baranwal, Mingfei Sun, Harshad Khadilkar, Stefano V. Albrecht,
• Abstract summary: In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards. We introduce Temporal-Agent Reward Redistribution (TAR$2$), a novel approach designed to address the agent-temporal credit assignment problem. TAR$2$ decomposes sparse global rewards into time-step-specific rewards and calculates agent-specific contributions to these rewards.
• Score: 14.003793644193605
• License:
• Abstract: In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to evaluate actions at intermediate time steps. We introduce Temporal-Agent Reward Redistribution (TAR$^2$), a novel approach designed to address the agent-temporal credit assignment problem by redistributing sparse rewards both temporally and across agents. TAR$^2$ decomposes sparse global rewards into time-step-specific rewards and calculates agent-specific contributions to these rewards. We theoretically prove that TAR$^2$ is equivalent to potential-based reward shaping, ensuring that the optimal policy remains unchanged. Empirical results demonstrate that TAR$^2$ stabilizes and accelerates the learning process. Additionally, we show that when TAR$^2$ is integrated with single-agent reinforcement learning algorithms, it performs as well as or better than traditional multi-agent reinforcement learning methods.

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