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06/05/2023
Data-Driven Regret Balancing for Online Model Selection in Bandits
We consider model selection for sequential decision making in stochastic...
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06/01/2023
Improving Offline RL by Blending Heuristics
We propose Heuristic Blending (HUBL), a simple performance-improving tec...
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11/09/2022
Leveraging Offline Data in Online Reinforcement Learning
Two central paradigms have emerged in the reinforcement learning (RL) co...
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10/27/2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
We study the role of the representation of state-action value functions ...
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05/21/2021
Parallelizing Contextual Linear Bandits
Standard approaches to decision-making under uncertainty focus on sequen...
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02/07/2021
Deep Reinforcement Learning with Dynamic Optimism
In recent years, deep off-policy actor-critic algorithms have become a d...
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01/19/2021
ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning
We introduce ES-ENAS, a simple neural architecture search (NAS) algorith...
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02/03/2020
Effective Diversity in Population-Based Reinforcement Learning
Maintaining a population of solutions has been shown to increase explora...
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06/11/2019
Wasserstein Reinforcement Learning
We propose behavior-driven optimization via Wasserstein distances (WDs) ...
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03/07/2019
