Minimax Strikes Back

• URL: http://arxiv.org/abs/2012.10700v1
• Date: Sat, 19 Dec 2020 14:42:41 GMT
• Title: Minimax Strikes Back
• Authors: Quentin Cohen-Solal and Tristan Cazenave
• Abstract summary: Deep Reinforcement Learning reaches a superhuman level of play in many complete information games. We take another approach to DRL using a Minimax algorithm instead of MCTS and learning only the evaluation of states, not the policy. We show that for multiple games it is competitive with the state of the art DRL for the learning performances and for the confrontations.
• Score: 10.485343576893865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Reinforcement Learning (DRL) reaches a superhuman level of play in many complete information games. The state of the art search algorithm used in combination with DRL is Monte Carlo Tree Search (MCTS). We take another approach to DRL using a Minimax algorithm instead of MCTS and learning only the evaluation of states, not the policy. We show that for multiple games it is competitive with the state of the art DRL for the learning performances and for the confrontations.

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