Papers › Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning

Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning

22 Sep 2023NeurIPS 2023 11arXiv:2309.12696archive 2025-07-28

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. 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.

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thu-rllab/CFCQL officialmentioned in papermentioned on GitHubpytorch report

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3ran · our draft was wrong
4ran
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EpisodeBatch thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository ran · metamorphic tier: deterministic no licence file found · pointer only · 4bf51e76634261e2 · report
Mixer thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository ran · metamorphic tier: deterministic no licence file found · pointer only · 007d9448d2cd2306 · report
QattenMixer thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository ran · metamorphic tier: deterministic no licence file found · pointer only · a8b96233900018b1 · report
VDNMixer thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository ran no licence file found · pointer only · 876bdefdb02890de · report
build_q_lambda_targets thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · a8e0e27ac8c6a7ec · report
get_parameters_num thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository ran · our draft was wrong no licence file found · pointer only · 6511fdf3dee84c1c · report
CQLearner thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository unverified no licence file found · pointer only · fc990af09cc00be8 · report
orthogonal_init_ thu-rllab/CFCQL/discrete/src/learners/cq_learner.py official repository unverified no licence file found · pointer only · f27d16af7e01f025 · report
build_td_lambda_targets identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · d95214ebaa722aa0 · report

Tasks

Multi-agent Reinforcement LearningOffline RLQ-LearningReinforcement Learningreinforcement-learning

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Q-Learning

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