Search Results for author: Yuzheng Zhuang

Found 9 papers, 1 papers with code

Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization

no code implementations4 Mar 2022 Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang

Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.

Imitation Learning Transfer Learning

Learning Symbolic Rules for Interpretable Deep Reinforcement Learning

no code implementations15 Mar 2021 Zhihao Ma, Yuzheng Zhuang, Paul Weng, Hankz Hankui Zhuo, Dong Li, Wulong Liu, Jianye Hao

To address this challenge and improve the transparency, we propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL.

reinforcement-learning

Robust Memory Augmentation by Constrained Latent Imagination

no code implementations1 Jan 2021 Yao Mu, Yuzheng Zhuang, Bin Wang, Wulong Liu, Shengbo Eben Li, Jianye Hao

The latent dynamics model summarizes an agent’s high dimensional experiences in a compact way.

Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets

no code implementations19 May 2020 Cong Fei, Bin Wang, Yuzheng Zhuang, Zongzhang Zhang, Jianye Hao, Hongbo Zhang, Xuewu Ji, Wulong Liu

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning.

Autonomous Vehicles Data Augmentation +1

Multi-Agent Interactions Modeling with Correlated Policies

1 code implementation ICLR 2020 Minghuan Liu, Ming Zhou, Wei-Nan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, Yong Yu

In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions.

Imitation Learning

Graph Attention Memory for Visual Navigation

no code implementations11 May 2019 Dong Li, Qichao Zhang, Dongbin Zhao, Yuzheng Zhuang, Bin Wang, Wulong Liu, Rasul Tutunov, Jun Wang

To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.

Graph Attention reinforcement-learning +1

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