activity
20192026
most citedBAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

31 citations · 58 across the 10 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

cs.LG2019★ 31 cited

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

Xinyue Chen, Zijian Zhou, Zheng Wang +3

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 int…

cs.LG2019★ 13 cited

Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

Che Wang, Yanqiu Wu, Quan Vuong +1

We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art performance but are also simple and minimalistic. For standard continuous control benchmarks, Soft…