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arXiv · 1910.12179

BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

Xinyue Chen·Zijian Zhou·Zheng Wang·Che Wang·Yanqiu Wu·Keith Ross
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Abstract

There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional interactions with the environment. We propose a new algorithm, Best-Action Imitation Learning (BAIL), which strives for both simplicity and performance. BAIL learns a V function, uses the V function to select actions it believes to be high-performing, and then uses those actions to train a policy network using imitation learning. For the MuJoCo benchmark, we provide a comprehensive experimental study of BAIL, comparing its performance to four other batch Q-learning and imitation-learning schemes for a large variety of batch datasets. Our experiments show that BAIL's performance is much higher than the other schemes, and is also computationally much faster than the batch Q-learning schemes.

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Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith Ross. 2019-10-27. BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning. https://arxiv.org/abs/1910.12179

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