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AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time

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Last updated 22 abril 2025
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
Implemented in one code library.
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
AlphaGo/AlphaGoZero/AlphaZero/MuZero: Mastering games using progressively fewer priors
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
AlphaZero and beyond: Polygames
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
Monte-Carlo Tree Search - Chessprogramming wiki
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
GitHub - CogitoNTNU/AlphaZero: An implementation of AlphaZero, trained to master Tic-Tac-Toe and Four in a row
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
Frontiers AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
Monte Carlo Tree Search (MCTS) in AlphaGo Zero, by Jonathan Hui
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
GitHub - kevaday/alphazero-general: A fast, generalized, and modified implementation of Deepmind's distinguished AlphaZero in PyTorch.
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
Lessons from AlphaZero (part 3): Parameter Tweaking, by Aditya Prasad, Oracle Developers
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
PDF) Alternative Loss Functions in AlphaZero-like Self-play
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
EfficientZero: How It Works — AI Alignment Forum
AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time
GitHub - kevaday/alphazero-general: A fast, generalized, and modified implementation of Deepmind's distinguished AlphaZero in PyTorch.
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