In recent years, one of the highest challenges in the field of artificial intelligence has been the creation of systems capable of learning how to play classic games. This paper presents a Deep Q-Learning based approach for playing the Snake game. All the elements of the related Reinforcement Learning framework are defined. Numerical simulations for both the training and the testing phases are presented. A particular focus is given to the associated Neural Network hyperparameters tuning, which is a crucial step in the agent design process and for the achievement of a desired target level of performance.
A deep Q-learning based approach applied to the snake game / Sebastianelli, A.; Tipaldi, M.; Ullo, S. L.; Glielmo, L.. - (2021), pp. 348-353. (Intervento presentato al convegno 29th Mediterranean Conference on Control and Automation, MED 2021 tenutosi a ita nel 2021) [10.1109/MED51440.2021.9480232].
A deep Q-learning based approach applied to the snake game
Glielmo L.
2021
Abstract
In recent years, one of the highest challenges in the field of artificial intelligence has been the creation of systems capable of learning how to play classic games. This paper presents a Deep Q-Learning based approach for playing the Snake game. All the elements of the related Reinforcement Learning framework are defined. Numerical simulations for both the training and the testing phases are presented. A particular focus is given to the associated Neural Network hyperparameters tuning, which is a crucial step in the agent design process and for the achievement of a desired target level of performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
