9 citations · 12 across the 8 of their papers we have counts for
3 papers · 1 filter
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Resolving the exploration-exploitation trade-off remains a fundamental problem in the design and implementation of reinforcement learning (RL) algorithms. In this paper, we focus o…
Bayesian Experience Reuse for Learning from Multiple Demonstrators
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Learning from demonstrations (LfD) improves the exploration efficiency of a learning agent by incorporating demonstrations from experts. However, demonstration data can often come…
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches. Ho…