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Entropy-Regularized Planning
- Speaker(s)
- Piotr Kozakowski
- Affiliation
- Uniwerystet Warszawski
- Date
- April 7, 2022, 12:15 p.m.
- Room
- room 3140
- Seminar
- Seminarium "Machine Learning"
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.
