Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning

1 code implementation • NeurIPS 2023 • Jianzhun Shao, Yun Qu, Chen Chen, Hongchang Zhang, Xiangyang Ji

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.

counterfactual Multi-agent Reinforcement Learning +3