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We employ the Deep Q-Learning algorithm with Experience Replay to train anagent capable of achieving a high-level of play in the L-Game whileself-learning from low-dimensional states. We also employ variable batch sizefor training in order to mitigate the loss of the rare reward signal andsignificantly accelerate training. Despite the large action space due to thenumber of possible moves, the low-dimensional state space and the rarity ofrewards, which only come at the end of a game, DQL is successful in training anagent capable of strong play without the use of any search methods or domainknowledge.

A Deep Q-Learning Agent for the L-Game with Variable Batch Training