Developing a Reinforcement Learning Agent for the Game of Checkers

Autor:	Andrea Dederichs-Koch, Daniel Schilberg, Henning Knauer
Rok vydání:	2020
Předmět:	Reward system Artificial neural network Action (philosophy) Simple (abstract algebra) business.industry Computer science Q-learning Reinforcement learning Artificial intelligence State (computer science) business
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030508951 AHFE (11)
Popis:	The aim of the following paper was to develop, test and evaluate a simple self-learning agent for the game of Checkers based on reinforcement learning and neural networks. The approach followed in this work is rather simple and based on a single deep neural network which is used to evaluate the board states and to choose the next best move for the agent. During the training phases the neural network is trained using a reward system based on different criteria derived from the prior and the current board state that resulted from the last action taken. The neural network takes a state-action pair, consisting of the current state and a possible move option and predicts the total reward that can be expected. This way all possible move options are evaluated and the one with the highest value is chosen as the next move.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::43ab6d6f6394449e4b55c5c722b93e47 https://doi.org/10.1007/978-3-030-50896-8_25 Zobrazit plný text záznamu