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Intelligent Bomberman with reinforcement learning

Ngo, T. (2021) Intelligent Bomberman with reinforcement learning.

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Abstract:Bomberman is a strategical, maze-based game where the players defeat their enemies by placing a multi-direction count-down bomb that would explode and destroy obstacles and other players. In this paper, a simplified version of Bomberman is implemented in Java, where different controlled agents are placed. Each agent represents one of five reinforcement learning methods: Q-Learning, Sarsa, Double Q-Learning, and Deep Q Neural Network with two state representations: 5-tiles information and complete information. Then, we investigate whether a specific reinforcement learning method can successfully learn to play Bomberman efficiently by evaluating them with ad-hoc agents and finally against each other. The configuration of 5-tiles information with Sarsa archives the best overall quantitative results.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
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