1,292 research outputs found
Learning to Search in Reinforcement Learning
In this thesis, we investigate the use of search based algorithms with deep neural
networks to tackle a wide range of problems ranging from board games to video
games and beyond. Drawing inspiration from AlphaGo, the first computer program
to achieve superhuman performance in the game of Go, we developed a new algorithm AlphaZero. AlphaZero is a general reinforcement learning algorithm that
combines deep neural networks with a Monte Carlo Tree search for planning and
learning. Starting completely from scratch, without any prior human knowledge
beyond the basic rules of the game, AlphaZero managed to achieve superhuman
performance in Go, chess and shogi. Subsequently, building upon the success of AlphaZero, we investigated ways to extend our methods to problems in which the rules
are not known or cannot be hand-coded. This line of work led to the development
of MuZero, a model-based reinforcement learning agent that builds a deterministic
internal model of the world and uses it to construct plans in its imagination. We
applied our method to Go, chess, shogi and the classic Atari suite of video-games,
achieving superhuman performance. MuZero is the first RL algorithm to master
a variety of both canonical challenges for high performance planning and visually complex problems using the same principles. Finally, we describe Stochastic
MuZero, a general agent that extends the applicability of MuZero to highly stochastic environments. We show that our method achieves superhuman performance in
stochastic domains such as backgammon and the classic game of 2048 while matching the performance of MuZero in deterministic ones like Go
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