Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
This addresses the problem of offline multi-agent RL for researchers and practitioners, offering a theoretically grounded method that scales independently of agent number, though it is incremental in building on single-agent conservative approaches.
The paper tackles the challenges of offline multi-agent reinforcement learning, such as distribution shift and high dimensionality, by proposing Counterfactual Conservative Q-Learning (CFCQL), which achieves superior performance over existing methods on most datasets, with significant margins in some cases.
Offline multi-agent reinforcement learning is challenging due to the coupling effect of both distribution shift issue common in offline setting and the high dimension issue common in multi-agent setting, making the action out-of-distribution (OOD) and value overestimation phenomenon excessively severe. Tomitigate this problem, we propose a novel multi-agent offline RL algorithm, named CounterFactual Conservative Q-Learning (CFCQL) to conduct conservative value estimation. Rather than regarding all the agents as a high dimensional single one and directly applying single agent methods to it, CFCQL calculates conservative regularization for each agent separately in a counterfactual way and then linearly combines them to realize an overall conservative value estimation. We prove that it still enjoys the underestimation property and the performance guarantee as those single agent conservative methods do, but the induced regularization and safe policy improvement bound are independent of the agent number, which is therefore theoretically superior to the direct treatment referred to above, especially when the agent number is large. We further conduct experiments on four environments including both discrete and continuous action settings on both existing and our man-made datasets, demonstrating that CFCQL outperforms existing methods on most datasets and even with a remarkable margin on some of them.