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
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.
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.
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.
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning
Reinforcement learning has seen great advancements in the past five years.
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
The goal of the reported experiment was to examine the performance of the KB-RL method in contrast to the Neural Network and to explore the capabilities of KB-RL to deliver a strong solution for the AI tasks.
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.