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MANSA: Learning Fast and Slow in Multi-Agent Systems
In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the …
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Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. …
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Quality-Similar Diversity via Population Based Reinforcement Learning
Diversity is a growing research topic in Reinforcement Learning (RL). Previous research on diversity has mainly focused on promoting …
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A Game-Theoretic Approach to Multi-agent Trust Region Optimization
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement …
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ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
Multi-agent reinforcement learning (MARL) suffers from the non-stationarity problem, which is the ever-changing targets at every …
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Contextual Transformer for Offline Meta Reinforcement Learning
The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and …
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TorchOpt: An Efficient Library for Differentiable Optimization
Recent years have witnessed the booming of various differentiable optimization algorithms. These algorithms exhibit different execution …
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Scalable Model-based Policy Optimization for Decentralized Networked Systems
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple …
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GenDexGrasp: Generalizable Dexterous Grasping
Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods …
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A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
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