Search Results for author: Can Chang

Found 2 papers, 1 papers with code

RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization

1 code implementation NeurIPS 2023 Zhecheng Yuan, Sizhe Yang, Pu Hua, Can Chang, Kaizhe Hu, Huazhe Xu

Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization.

Out-of-Distribution Generalization reinforcement-learning

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

no code implementations5 Dec 2022 Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu

Specifically, we introduce Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance(E-MAPP), a novel framework that leverages parallel programs to guide multiple agents to efficiently accomplish goals that require planning over $10+$ stages.

Multi-agent Reinforcement Learning reinforcement-learning +2

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