Search Results for author: Guangxiang Zhu

Found 10 papers, 5 papers with code

Offline Reinforcement Learning with Reverse Model-based Imagination

no code implementations NeurIPS 2021 Jianhao Wang, Wenzhe Li, Haozhe Jiang, Guangxiang Zhu, Siyuan Li, Chongjie Zhang

These reverse imaginations provide informed data augmentation for model-free policy learning and enable conservative generalization beyond the offline dataset.

Data Augmentation Offline RL +1

On the Estimation Bias in Double Q-Learning

1 code implementation NeurIPS 2021 Zhizhou Ren, Guangxiang Zhu, Hao Hu, Beining Han, Jianglun Chen, Chongjie Zhang

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation.

Q-Learning Value prediction

Generalizable Episodic Memory for Deep Reinforcement Learning

1 code implementation11 Mar 2021 Hao Hu, Jianing Ye, Guangxiang Zhu, Zhizhou Ren, Chongjie Zhang

Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning.

Atari Games Continuous Control +1

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

1 code implementation NeurIPS 2020 Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang

It maximizes the mutual information between imaginary and real trajectories so that the policy improvement learned from imaginary trajectories can be easily generalized to real trajectories.

Model-based Reinforcement Learning reinforcement-learning

Object-Oriented Model Learning through Multi-Level Abstraction

no code implementations ICLR 2019 Guangxiang Zhu, Jianhao Wang, Zhizhou Ren, Chongjie Zhang

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability.

Relational Reasoning Self-Supervised Learning

Object-Oriented Dynamics Learning through Multi-Level Abstraction

1 code implementation16 Apr 2019 Guangxiang Zhu, Jianhao Wang, Zhizhou Ren, Zichuan Lin, Chongjie Zhang

We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability.

Relational Reasoning Self-Supervised Learning

Context-Aware Policy Reuse

no code implementations11 Jun 2018 Siyuan Li, Fangda Gu, Guangxiang Zhu, Chongjie Zhang

Transfer learning can greatly speed up reinforcement learning for a new task by leveraging policies of relevant tasks.

reinforcement-learning Transfer Learning

Object-Oriented Dynamics Predictor

1 code implementation NeurIPS 2018 Guangxiang Zhu, Zhiao Huang, Chongjie Zhang

Generalization has been one of the major challenges for learning dynamics models in model-based reinforcement learning.

Model-based Reinforcement Learning

Generative Adversarial Mapping Networks

no code implementations28 Sep 2017 Jianbo Guo, Guangxiang Zhu, Jian Li

They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution.

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