29 citations · 43 across the 13 of their papers we have counts for
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cs.LG2021
The -Divergence Reinforcement Learning Framework
Chen Gong, Qiang He, Yunpeng Bai +6
The framework of deep reinforcement learning (DRL) provides a powerful and widely applicable mathematical formalization for sequential decision-making. This paper present a novel D…
cs.LG2021★ 6 cited
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning
Qiang He, Huangyuan Su, Chen Gong +1
During the training of a reinforcement learning (RL) agent, the distribution of training data is non-stationary as the agent's behavior changes over time. Therefore, there is a ris…
cs.LG2021★ 2 cited
POPO: Pessimistic Offline Policy Optimization
Qiang He, Xinwen Hou
Offline reinforcement learning (RL), also known as batch RL, aims to optimize policy from a large pre-recorded dataset without interaction with the environment. This setting offers…