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Found 5 papers, 2 papers with code
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem.
Learning Generalizable Agents via Saliency-Guided Features Decorrelation
Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
However, constrained by the limited quality of the offline dataset, its performance is often sub-optimal.
Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning
Recent works have shown the potential of diffusion models in computer vision and natural language processing.
Distributional Reward Estimation for Effective Multi-Agent Deep Reinforcement Learning
Our main idea is to design the multi-action-branch reward estimation and policy-weighted reward aggregation for stabilized training.
Multi-agent Reinforcement Learning
reinforcement-learning
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