Abstract
The multi-agent path planning problem is usually addressed via the use of reinforcement learning methods. In this article, a new Lludo game path planning algorithm based on Q-learning was proposed, and a set of cooperation proprieties that the agents have to follow in order to reach the goal location. To accomplish that a Lludo game environment modeling is introduced according to the Markov decision process (MDP) principles. The main objective of this work is to increase agents’ cooperation rate with the aim of decreasing the execution time of the assigned tasks. To demonstrate the improvement produced by this proposal, the path planning algorithm is applied in comparison with a greedy strategy also based on Q-learning. During this comparison, the execution time as well as the agents’ reward acquisition were examined in different cases. The simulation results reflect the advantages provided by this new algorithm in comparison with related work.
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