Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

2020-10-23 · NeurIPS 2020 12 · Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang

Sample efficiency has been one of the major challenges for deep reinforcement learning. Recently, model-based reinforcement learning has been proposed to address this challenge by performing planning on imaginary trajectories with a learned world model. However, world model learning may suffer from overfitting to training trajectories, and thus model-based value estimation and policy search will be pone to be sucked in an inferior local policy. In this paper, we propose a novel model-based reinforcement learning algorithm, called BrIdging Reality and Dream (BIRD). 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. We demonstrate that our approach improves sample efficiency of model-based planning, and achieves state-of-the-art performance on challenging visual control benchmarks.

📄 PDF Abstract BibTeX arXiv:2010.12142

Code (1)

Mehooz/BIRD_code 공식 구현 tf

Tasks

Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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