Michael Gimelfarb

Ayal Taitler

CoRR, 2024

2023

Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions.

[BibT_eX]

[DOI]

Michael Jong Kim

CoRR, 2023

Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

pyRDDLGym: From RDDL to Gym Environments.

[BibT_eX]

[DOI]

Ayal Taitler

Sriram Gopalakrishnan

Xiaotian Liu

CoRR, 2022

A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation.

[BibT_eX]

[DOI]

CoRR, 2021

Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Risk-Aware Transfer in Reinforcement Learning using Successor Features.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bayesian Experience Reuse for Learning from Multiple Demonstrators.

[BibT_eX]

[DOI]

Mike Gimelfarb

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020

ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Contextual Policy Reuse using Deep Mixture Models.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018

Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach.

[BibT_eX]

[DOI]