Ronan Fruit

Bio:

R. Fruit is a third year PhD student in the SequeL team at Inria under the supervision of Alessandro Lazaric and Daniil Ryabko. He is currently research intern at Facebook AI Research (FAIR) Montreal. His research focuses on the theoretical understanding of the exploration-exploitation dilemma in Reinforcement Learning and the design of algorithms with provably good regret guarantees.

Tutorial: Exploration-Exploitation in Reinforcement Learning

Reinforcement Learning (RL) studies the problem of sequential decision-making when the environment (i.e., the dynamics and the reward) is initially unknown but can be learned through direct interaction. A crucial step in the learning problem is to properly balance the exploration of the environment, in order to gather useful information, and the exploitation of the learned policy to collect as much reward as possible. Recent theoretical results proved that approaches based on optimism or posterior sampling (e.g., UCRL, PSRL, etc.) successfully solve the exploration-exploitation dilemma and may require exponentially less samples than simpler (but very popular) techniques such as epsilon-greedy to converge to near-optimal policies. While the optimism and posterior sampling principles are directly inspired by multi-armed bandit literature, RL poses specific challenges (e.g., how “local” uncertainty propagates through the Markov dynamics), which requires a more sophisticated theoretical analysis. The focus of the tutorial is to provide a formal definition of the exploration-exploitation dilemma, discuss its challenges, and to review the main algorithmic principles and their theoretical guarantees for different optimality criteria (notably finite-horizon and average-reward problems). Throughout the whole tutorial we will discuss open problems and possible future research directions.