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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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Debias the Black-Box: A Fair Ranking Framework via Knowledge Distillation
Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many …
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Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Large sequence models (SM) such as GPT series and BERT have displayed outstanding performance and generalization capabilities in …
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On the Convergence of Fictitious Play: A Decomposition Approach
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in n-player …
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Neural Auto-Curricula in Two-Player Zero-Sum Games
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, …
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