Game of Go
19 papers with code • 1 benchmarks • 1 datasets
Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent. The task is to train an agent to play the game and be superior to other players.
Benchmarks
These leaderboards are used to track progress in Game of Go
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Libraries
Use these libraries to find Game of Go models and implementations
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Most implemented papers
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
pytorch/ELF
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The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy.
Hyper-Parameter Sweep on AlphaZero General
Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how these parameters contribute to training.
The Computational Limits of Deep Learning
Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks.
Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection
This gives an intrinsic strength measurement for the neural network.
Visualizing MuZero Models
kaesve/muzero
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ICML Workshop URL 2021
In contrast to standard forward dynamics models that predict a full next state, value equivalent models are trained to predict a future value, thereby emphasizing value relevant information in the representations.
Conservative Optimistic Policy Optimization via Multiple Importance Sampling
WolfLo/optimist
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Reinforcement Learning (RL) has been able to solve hard problems such as playing Atari games or solving the game of Go, with a unified approach.
Learning and Planning in Complex Action Spaces
Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement.
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
lan-lc/adversarial_example_of_Go
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Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions.
Active Reinforcement Learning for Robust Building Control
Reinforcement learning (RL) is a powerful tool for optimal control that has found great success in Atari games, the game of Go, robotic control, and building optimization.
