Jun 20, 2018 · This paper presents and evaluates a novel algorithm that based on Monte Carlo sampling as terminal states' estimation method in reinforce ...
This paper presents and evaluates a novel algorithm that based on Monte Carlo sampling as terminal states' estimation method in reinforce learning systems. The ...
Sep 15, 2021 · While computation remains an important aspect in the learning setting, this course will largely focus on sample efficiency, that is, the problem ...
3To approximate the oracle in problems with large state spaces, we can use Sparse Sampling (Kearns, Mansour, and Ng 2002) or any Monte-Carlo tree search methods ...
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This paper introduces two innovative methods: the Opponent Model and Optimized Deep Monte-Carlo (ODMC). These methods are designed to improve the training ...
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May 6, 2024 · We address these challenges by proposing a novel guided exploration method that uses an ensemble of Monte Carlo Critics for calculating ...
May 12, 2023 · AlphaZero-like frameworks which combine Monte-Carlo tree search with reinforcement learning have been successfully applied to numerous games.
Scalable sub-game solving for imperfect-information games
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Nov 14, 2021 · By combining a Monte Carlo tree search (MCTS) and deep reinforcement learning, AlphaGo defeated a Go world champion [1], [2], [3], [4], [5]. The ...
This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player ...
May 12, 2023 · AlphaZero-like frameworks which combine Monte-Carlo tree search with reinforcement learning have been successfully applied to numerous games ...