Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach. - Michael Gimelfarb; Scott Sanner; Chi-Guhn Lee (NeurIPS, 2018)

Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. - Michael Gimelfarb; Scott Sanner; Chi-Guhn Lee (UAI, 2019)

Contextual Policy Reuse using Deep Mixture Models. - Michael Gimelfarb; Scott Sanner; Chi-Guhn Lee (CoRR, 2020)

ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. - Michael Gimelfarb; Scott Sanner; Chi-Guhn Lee (CoRR, 2020)

Bayesian Experience Reuse for Learning from Multiple Demonstrators. - Mike Gimelfarb; Scott Sanner; Chi-Guhn Lee (IJCAI, 2021)

Risk-Aware Transfer in Reinforcement Learning using Successor Features. - Michael Gimelfarb; André Barreto 001; Scott Sanner; Chi-Guhn Lee (NeurIPS, 2021)

Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts. - Michael Gimelfarb; Scott Sanner; Chi-Guhn Lee (UAI, 2021)

RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation. - Noah Patton; Jihwan Jeong; Michael Gimelfarb; Scott Sanner (CoRR, 2021)

A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs. - Noah Patton; Jihwan Jeong; Mike Gimelfarb; Scott Sanner (AAAI, 2022)

pyRDDLGym: From RDDL to Gym Environments. - Ayal Taitler; Michael Gimelfarb; Sriram Gopalakrishnan; Xiaotian Liu; Scott Sanner (CoRR, 2022)

Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization. - Jihwan Jeong; Xiaoyu Wang; Michael Gimelfarb; Hyunwoo Kim; Baher Abdulhai; Scott Sanner (ICLR, 2023)

Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions. - Michael Gimelfarb; Michael Jong Kim (CoRR, 2023)

JaxPlan and GurobiPlan: Optimization Baselines for Replanning in Discrete and Mixed Discrete-Continuous Probabilistic Domains. - Michael Gimelfarb; Ayal Taitler; Scott Sanner (ICAPS, 2024)

The 2023 International Planning Competition. - Ayal Taitler; Ron Alford; Joan Espasa; Gregor Behnke; Daniel Fiser; Michael Gimelfarb; Florian Pommerening; Scott Sanner; Enrico Scala; Dominik Schreiber 001; Javier Segovia-Aguas; Jendrik Seipp (AI Mag., 2024)

Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs. - Michael Gimelfarb; Ayal Taitler; Scott Sanner (CoRR, 2024)

ModelDiff: Symbolic Dynamic Programming for Model-Aware Policy Transfer in Deep Q-Learning. - Xiaotian Liu; Jihwan Jeong; Ayal Taitler; Michael Gimelfarb; Scott Sanner (AAAI, 2025)

Bounded-Error Policy Optimization for Mixed Discrete-Continuous MDPs via Constraint Generation in Nonlinear Programming. - Michael Gimelfarb; Ayal Taitler; Scott Sanner (CPAIOR (1), 2025)

STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation. - Hossein Goli; Michael Gimelfarb; Nathan Samuel de Lara; Haruki Nishimura; Masha Itkina; Florian Shkurti (CoRR, 2025)