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
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Libraries
Use these libraries to find Game of Go models and implementations
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Latest papers with no code
Mobile Networks for Computer Go
The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines.
The Go Transformer: Natural Language Modeling for Game Play
This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go.
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning
A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of 82 steps, albeit with large computational resources.
Multi-step Greedy Reinforcement Learning Algorithms
We derive model-free RL algorithms based on $\kappa$-PI and $\kappa$-VI in which the surrogate problem can be solved by any discrete or continuous action RL method, such as DQN and TRPO.
MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.
Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning
In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration.
Playing Go without Game Tree Search Using Convolutional Neural Networks
We introduce three structures and training methods that aim to create a strong Go player: non-rectangular convolutions, which will better learn the shapes on the board, supervised learning, training on a data set of 53, 000 professional games, and reinforcement learning, training on games played between different versions of the network.
Designing Game of Theorems
"Theorem proving is similar to the game of Go.
Building a Computer Mahjong Player via Deep Convolutional Neural Networks
The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program.
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning
Reinforcement learning has seen great advancements in the past five years.
