Game of Go
14 papers with code • 1 benchmarks • 0 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 implementationsLatest papers with no code
Spatial State-Action Features for General Games
In this paper, we formulate a design and efficient implementation of spatial state-action features for general games.
Planning in Stochastic Environments with a Learned Model
However, previous instantiations of this approach were limited to the use of deterministic models.
Probabilistic DAG Search
Exciting contemporary machine learning problems have recently been phrased in the classic formalism of tree search -- most famously, the game of Go.
Learning and Planning in Complex Action Spaces
Instead, only small subsets of actions can be sampled for the purpose of policy evaluation and improvement.
Batch Monte Carlo Tree Search
The transposition table contains the results of the inferences while the search tree contains the statistics of Monte Carlo Tree Search.
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.
