Game of Go

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (62 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
  5 Dec 2017 62 repositories listed Syntology ran 13 of 17 samples · 4 unverified · 9 pointer-only (licence)
  The game of chess is the most widely-studied domain in the history of artificial intelligence.
• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
  19 Nov 2019 18 repositories listed Syntology ran 43 of 64 samples · 21 unverified · 62 pointer-only (licence)
  When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.
• Accelerating Self-Play Learning in Go
  27 Feb 2019 6 repositories listed
  By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods.
• Better Computer Go Player with Neural Network and Long-term Prediction
  19 Nov 2015 3 repositories listed
  Against human players, the newest versions, darkfores2, achieve a stable 3d level on KGS Go Server as a ranked bot, a substantial improvement upon the estimated 4k-5k ranks for DCNN reported in Clark & Storkey (2015)…
• Active Reinforcement Learning for Robust Building Control
  16 Dec 2023 2 repositories listed
  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.
• Planning in Stochastic Environments with a Learned Model
  29 Sep 2021 2 repositories listed
  However, previous instantiations of this approach were limited to the use of deterministic models.
• MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning
  16 Jun 2019 2 repositories listed
  By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models.
• Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games
  12 Aug 2024 1 repository listed
  Defining and measuring decision-making styles, also known as playstyles, is crucial in gaming, where these styles reflect a broad spectrum of individuality and diversity.
• Monte Carlo Tree Search with Boltzmann Exploration
  11 Apr 2024 1 repository listed
  Monte-Carlo Tree Search (MCTS) methods, such as Upper Confidence Bound applied to Trees (UCT), are instrumental to automated planning techniques.
• Are AlphaZero-like Agents Robust to Adversarial Perturbations?
  7 Nov 2022 1 repository listed
  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.
• Learning and Planning in Complex Action Spaces
  13 Apr 2021 1 repository listed
  Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement.
• Conservative Optimistic Policy Optimization via Multiple Importance Sampling
  4 Mar 2021 1 repository listed
  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.
• Visualizing MuZero Models
  25 Feb 2021 1 repository listed
  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.
• Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection
  3 Sep 2020 1 repository listed
  This gives an intrinsic strength measurement for the neural network.
• The Computational Limits of Deep Learning
  10 Jul 2020 1 repository listed
  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.
• Hyper-Parameter Sweep on AlphaZero General
  19 Mar 2019 1 repository listed
  Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how these parameters contribute to training.
• ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
  12 Feb 2019 1 repository listed
  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…
• FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go
  16 Jul 2017 1 repository listed
  In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human-Machine Cooperative System (FHMCS) for the game…
• Move Evaluation in Go Using Deep Convolutional Neural Networks
  20 Dec 2014 1 repository listed
  The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function.
• Teaching Deep Convolutional Neural Networks to Play Go
  10 Dec 2014 1 repository listed
  Our final networks are able to achieve move prediction accuracies of 41.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.