RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
This addresses the challenge of training coordinating agents in complex environments for multi-agent reinforcement learning, though it appears incremental as it builds on existing centralized training with decentralized execution frameworks.
The paper tackled the problem of risk-neutral policies failing in complex cooperative multi-agent reinforcement learning due to reward randomness and environmental uncertainty, and proposed RMIX, a method using Conditional Value at Risk (CVaR) to learn risk-sensitive policies, which significantly outperformed state-of-the-art methods on StarCraft II tasks with enhanced coordination and improved sample efficiency.
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR for decentralized execution. Then, to handle the temporal nature of the stochastic outcomes during executions, we propose a dynamic risk level predictor for risk level tuning. Finally, we optimize the CVaR policies with CVaR values used to estimate the target in TD error during centralized training and the CVaR values are used as auxiliary local rewards to update the local distribution via Quantile Regression loss. Empirically, we show that our method significantly outperforms state-of-the-art methods on challenging StarCraft II tasks, demonstrating enhanced coordination and improved sample efficiency.