Entropy-Regularized Planning

Prelegent(ci)

Piotr Kozakowski

Afiliacja

Uniwerystet Warszawski

Termin

7 kwietnia 2022 12:15

Pokój

p. 3140

Seminarium

Seminarium "Uczenie maszynowe"

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Recent works have shown the effectiveness of entropy regularization in Monte Carlo Tree Search (MCTS). In this presentation I will first introduce the framework of Maximum Entropy Reinforcement Learning and show how it can be applied to MCTS. Then I will present various variants of entropy regularization. Next I will explain how the relative entropy regularization can be applied to a planning-learning system akin to MuZero and what particular benefits it can bring to planning with a learned model. Finally I will show our preliminary results on the Atari 100K benchmark.

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