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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
N/A
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A Unified Diversity Measure for Multiagent Reinforcement Learning
Promoting behavioural diversity is of critical importance in multi-agent reinforcement learning, since it helps the agent population …
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Constrained Update Projection Approach to Safe Policy Optimization
Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring …
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MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control
We introduce the Multi-Agent Tracking Environment (MATE), a novel multi-agent environment simulates the target coverage control …
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Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning
Setting up a well-designed reward function has been challenging for many reinforcement learning applications. Preference-based …
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Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation even at the …
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End-to-End Affordance Learning for Robotic Manipulation
Learning to manipulate 3D objects in an interactive environment has been a challenging problem in Reinforcement Learning (RL). In …
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