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2020 – today
- 2024
[j1]Ayal Taitler
, Ron Alford, Joan Espasa, Gregor Behnke, Daniel Fiser, Michael Gimelfarb, Florian Pommerening, Scott Sanner, Enrico Scala, Dominik Schreiber, Javier Segovia-Aguas, Jendrik Seipp:
The 2023 International Planning Competition. AI Mag. 45(2): 280-296 (2024)- [c8]Michael Gimelfarb, Ayal Taitler, Scott Sanner:
JaxPlan and GurobiPlan: Optimization Baselines for Replanning in Discrete and Mixed Discrete-Continuous Probabilistic Domains. ICAPS 2024: 230-238 - [i9]Michael Gimelfarb, Ayal Taitler, Scott Sanner:
Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs. CoRR abs/2401.12243 (2024) - 2023
- [c7]Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, Scott Sanner:
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization. ICLR 2023 - [i8]Michael Gimelfarb, Michael Jong Kim:
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions. CoRR abs/2305.07844 (2023) - 2022
- [c6]Noah Patton, Jihwan Jeong, Mike Gimelfarb, Scott Sanner:
A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs. AAAI 2022: 9894-9901 - [i7]Jihwan Jeong, Xiaoyu Wang, Michael Gimelfarb, Hyunwoo Kim, Baher Abdulhai, Scott Sanner:
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization. CoRR abs/2210.03802 (2022) - [i6]Ayal Taitler, Michael Gimelfarb, Sriram Gopalakrishnan, Xiaotian Liu, Scott Sanner:
pyRDDLGym: From RDDL to Gym Environments. CoRR abs/2211.05939 (2022) - 2021
- [c5]Mike Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Bayesian Experience Reuse for Learning from Multiple Demonstrators. IJCAI 2021: 2425-2431 - [c4]Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee:
Risk-Aware Transfer in Reinforcement Learning using Successor Features. NeurIPS 2021: 17298-17310 - [c3]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts. UAI 2021: 1787-1797 - [i5]Michael Gimelfarb, André Barreto, Scott Sanner, Chi-Guhn Lee:
Risk-Aware Transfer in Reinforcement Learning using Successor Features. CoRR abs/2105.14127 (2021) - [i4]Noah Patton, Jihwan Jeong, Michael Gimelfarb, Scott Sanner:
RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation. CoRR abs/2106.07260 (2021) - 2020
- [i3]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Contextual Policy Reuse using Deep Mixture Models. CoRR abs/2003.00203 (2020) - [i2]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Bayesian Experience Reuse for Learning from Multiple Demonstrators. CoRR abs/2006.05725 (2020) - [i1]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
ε-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. CoRR abs/2007.00869 (2020)
2010 – 2019
- 2019
- [c2]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Epsilon-BMC: A Bayesian Ensemble Approach to Epsilon-Greedy Exploration in Model-Free Reinforcement Learning. UAI 2019: 476-485 - 2018
- [c1]Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee:
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach. NeurIPS 2018: 9549-9559
Coauthor Index
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