Thinking Fast and Slow with Deep Learning and Tree Search
Published in
Neural Information Processing Systems(2017)External Links:
TL
TL;DR
This paper presents Expert Iteration (ExIt), a novel reinforcement learning algorithm which decomposes the problem into separate planning and generalisation tasks, and shows that ExIt outperforms REINFORCE for training a neural network to play the board game Hex, and the final tree search agent, trained tabula rasa, defeats MoHex 1.0.
Abstract
Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans. In this paper, we present Expert Iteration (ExIt), a novel reinforcement learning algorithm which decomposes the problem into separate planning and generalisation tasks. Planning new policies is performed by tree search, while a deep neural network generalises those plans. Subsequently, tree search is improved by using the neural network policy to guide search, increasing the strength of new plans. In contrast, standard deep Reinforcement Learning algorithms rely on a neural network not only to generalise plans, but to discover them too. We show that ExIt outperforms REINFORCE for training a neural network to play the board game Hex, and our final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most recent Olympiad Champion player to be publicly released.
References24 items
1
TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning
2
Mastering the game of Go without human knowledge
3
Neurohex: A Deep Q-learning Hex Agent
4
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
5