How can I better assign rewards in multi-agent RL?
TAR²: Temporal-Agent Reward Redistribution for Optimal Policy Preservation in Multi-Agent Reinforcement Learning
February 10, 2025
https://arxiv.org/pdf/2502.04864-
The Main Topic: This paper introduces TAR² (Temporal-Agent Reward Redistribution), a new method for training AI agents that work together in environments where rewards are only given at the end of a task (episodic rewards), like winning a game. TAR² helps solve the "credit assignment problem"—figuring out which agent deserves how much credit for the final outcome and when those actions that contribute to winning took place—by intelligently distributing the final reward across both agents and timesteps.
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Key Points for LLM-based Multi-Agent Systems:
- Sparse Rewards: TAR² is specifically designed for scenarios with sparse rewards, like many realistic applications of LLMs where feedback is infrequent or delayed.
- Credit Assignment: Effectively distributing credit is crucial for training cooperative LLM agents. TAR² offers a principled approach to achieve this, potentially improving coordination and learning efficiency.
- Policy Preservation: TAR² is theoretically grounded, ensuring it doesn't change the optimal strategy (policy) of the agents, only how quickly they learn it. This is important for maintaining desired behavior in LLMs.
- Dual Attention Mechanism: TAR² employs a dual attention mechanism, allowing it to focus on both when important actions occur and which agents are responsible. This is relevant for understanding and debugging complex interactions between LLM agents.
- Potential for Interpretability: Although not the primary focus, TAR²’s reward redistribution can offer insights into individual agent contributions and strategic shifts throughout an interaction, which could be useful for analyzing LLM behavior.