9 citations · 9 across the 4 of their papers we have counts for
5 papers · 1 filter
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation
Noah Patton, Jihwan Jeong, Michael Gimelfarb +1
Planning provides a framework for optimizing sequential decisions in complex environments. Recent advances in efficient planning in deterministic or stochastic high-dimensional dom…
Risk-Aware Transfer in Reinforcement Learning using Successor Features
Michael Gimelfarb, André Barreto, Scott Sanner +1
Sample efficiency and risk-awareness are central to the development of practical reinforcement learning (RL) for complex decision-making. The former can be addressed by transfer le…
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…