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Designing generalizable agents capable of adapting to diverse embodiments has achieved significant attention in Reinforcement Learning (RL), which is critical for deploying RL agents in various real-world applications.
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Diversity is all you need: Learning skills without a reward function
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Language models are few-shot learners
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Context-aware dynamics model for generalization in model-based reinforcement learning
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
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Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
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Learning robust state abstractions for hidden-parameter block mdps
Amy Zhang, Shagun Sodhani, Khimya Khetarpal, and Joelle Pineau · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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The information geometry of unsupervised reinforcement learning
Benjamin Eysenbach, Ruslan Salakhutdinov, and Sergey Levine · 2021
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Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability
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Embodied intelligence via learning and evolution
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Aps: Active pretraining with successor features
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
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Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions
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Isaac gym: High performance gpu based physics simulation for robot learning
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Cross-domain random pre-training with prototypes for reinforcement learning
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Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, and Shuran Song · 2023
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Rushuai Yang, Chenjia Bai, Hongyi Guo, Siyuan Li, Bin Zhao, Zhen Wang, Peng Liu, and Xuelong Li · 2023
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Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Songming Liu, Dong Yan, and Jun Zhu · 2023
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Multi-embodiment legged robot control as a sequence modeling problem
Chen Yu, Weinan Zhang, Hang Lai, Zheng Tian, Laurent Kneip, and Jun Wang · 2023
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Automatic intrinsic reward shaping for exploration in deep reinforcement learning
Mingqi Yuan, Bo Li, Xin Jin, and Wenjun Zeng · 2023
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Robot parkour learning
Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher G Atkeson, Sören Schwertfeger, Chelsea Finn, and Hang Zhao · 2023
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Pushing the limits of cross-embodiment learning for manipulation and navigation
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