52 citations · 222 across the 17 of their papers we have counts for
Showing 2019Show all
3 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…
cs.LG2019★ 40 cited
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past
Che Wang, Keith Ross
Soft Actor-Critic (SAC) is an off-policy actor-critic deep reinforcement learning (DRL) algorithm based on maximum entropy reinforcement learning. By combining off-policy updates w…