15 citations · 45 across the 14 of their papers we have counts for
3 papers · 1 filter
Average-Reward Learning and Planning with Options
Yi Wan, Abhishek Naik, Richard S. Sutton
We extend the options framework for temporal abstraction in reinforcement learning from discounted Markov decision processes (MDPs) to average-reward MDPs. Our contributions includ…
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…
Average-Reward Off-Policy Policy Evaluation with Function Approximation
Shangtong Zhang, Yi Wan, Richard S. Sutton +1
We consider off-policy policy evaluation with function approximation (FA) in average-reward MDPs, where the goal is to estimate both the reward rate and the differential value func…