activity
20192026
most citedDeep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization

2 citations · 4 across the 7 of their papers we have counts for

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Showing 2021Show all

2 papers · 1 filter

cs.LG2021

Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization

Yufei Kuang, Miao Lu, Jie Wang +3

Deep reinforcement learning algorithms can perform poorly in real-world tasks due to the discrepancy between source and target environments. This discrepancy is commonly viewed as…

cs.LG2021

Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic

Zhihai Wang, Jie Wang, Qi Zhou +2

Model-based reinforcement learning algorithms, which aim to learn a model of the environment to make decisions, are more sample efficient than their model-free counterparts. The sa…