40 citations · 70 across the 3 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning with Automated Auxiliary Loss Search
Tairan He, Yuge Zhang, Kan Ren +5
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative…
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
Xinyue Chen, Che Wang, Zijian Zhou +1
Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchma…
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…
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…
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…