Game of Go
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.
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Latest papers without code
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.
MoËT: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees
We propose Mo\"ET, 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.
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning
Reinforcement learning has seen great advancements in the past five years.
Hyper-Parameter Sweep on AlphaZero General
Therefore, in this paper, we choose 12 parameters in AlphaZero and evaluate how these parameters contribute to training.
A GFML-based Robot Agent for Human and Machine Cooperative Learning on Game of Go
This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart machine cooperative learning system on game of Go.
Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game
We compare a novel Knowledge-based Reinforcement Learning (KB-RL) approach with the traditional Neural Network (NN) method in solving a classical task of the Artificial Intelligence (AI) field.
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction
This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications.
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning
This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces.
Pressure Predictions of Turbine Blades with Deep Learning
Deep learning has been used in many areas, such as feature detections in images and the game of go.