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cs.LG2024
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey
Zhihong Liu, Xin Xu, Peng Qiao +1
Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size…
cs.LG2024
Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning
Tenglong Liu, Yang Li, Yixing Lan +3
In offline reinforcement learning, the challenge of out-of-distribution (OOD) is pronounced. To address this, existing methods often constrain the learned policy through policy reg…