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Poster
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
Xinyue Chen · Zijian Zhou · Zheng Wang · Che Wang · Yanqiu Wu · Keith Ross

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #617

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.

Author Information

Xinyue Chen (NYU Shanghai)
Zijian Zhou (NYU Shanghai)
Zheng Wang (NYU Shanghai)
Che Wang (New York University)
Yanqiu Wu (New York University)
Keith Ross (NYU Shanghai)

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