10 citations · 10 across the 2 of their papers we have counts for
3 papers
cs.AI2021
Planning with Expectation Models for Control
Katya Kudashkina, Yi Wan, Abhishek Naik +1
In model-based reinforcement learning (MBRL), Wan et al. (2019) showed conditions under which the environment model could produce the expectation of the next feature vector rather…
cs.LG2020★ 10 cited
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)
Zhimin Hou, Kuangen Zhang, Yi Wan +3
The optimal policy of a reinforcement learning problem is often discontinuous and non-smooth. I.e., for two states with similar representations, their optimal policies can be signi…
cs.LG2019
Planning with Expectation Models
Yi Wan, Zaheer Abbas, Adam White +2
Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the…