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Found 5 papers, 1 papers with code
Supported Trust Region Optimization for Offline Reinforcement Learning
Offline reinforcement learning suffers from the out-of-distribution issue and extrapolation error.
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
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.
Wasserstein Unsupervised Reinforcement Learning
These pre-trained policies can accelerate learning when endowed with external reward, and can also be used as primitive options in hierarchical reinforcement learning.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Reducing Conservativeness Oriented Offline Reinforcement Learning
In offline reinforcement learning, a policy learns to maximize cumulative rewards with a fixed collection of data.
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning
Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning.
