How can I better assign rewards in multi-agent RL?
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
This paper introduces Temporal-Agent Reward Redistribution (TAR²), a method for improving multi-agent reinforcement learning in scenarios with sparse or delayed rewards, like those often encountered in long-horizon tasks. TAR² redistributes a single episodic reward across both individual agents and timesteps within an episode, creating a denser reward signal. This is proven to be equivalent to potential-based reward shaping, meaning the optimal policy under the reshaped reward function is also optimal under the original sparse reward. The impact of faulty credit assignment on policy gradient variance is analyzed, showing that poor credit distribution increases variance and slows learning. Experiments in SMACLite demonstrate TAR²'s sample efficiency.
Key points for LLM-based multi-agent systems:
- Reward Shaping for LLMs: TAR² offers a strategy for reward shaping in multi-agent LLM systems where evaluating individual contributions within a complex, long-horizon interaction is difficult. The denser reward signal could improve LLM training convergence and sample efficiency.
- Credit Assignment in Collaborative LLMs: The paper's analysis of credit assignment is crucial for collaborative LLM scenarios. Understanding how to attribute credit to individual LLMs for a joint outcome directly relates to how these models can learn effective cooperative strategies.
- Simplified Training for Multi-Agent LLMs: TAR² enables the use of single-agent RL algorithms for training multi-agent systems. This simplifies training and potentially improves scalability when applied to large language models.