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- W4293456039 abstract "We employ the Deep Q-Learning algorithm with Experience Replay to train an agent capable of achieving a high-level of play in the L-Game while self-learning from low-dimensional states. We also employ variable batch size for training in order to mitigate the loss of the rare reward signal and significantly accelerate training. Despite the large action space due to the number of possible moves, the low-dimensional state space and the rarity of rewards, which only come at the end of a game, DQL is successful in training an agent capable of strong play without the use of any search methods or domain knowledge." @default.
- W4293456039 created "2022-08-29" @default.
- W4293456039 creator A5047221264 @default.
- W4293456039 creator A5072314554 @default.
- W4293456039 date "2018-02-17" @default.
- W4293456039 modified "2023-09-27" @default.
- W4293456039 title "A Deep Q-Learning Agent for the L-Game with Variable Batch Training" @default.
- W4293456039 doi "https://doi.org/10.48550/arxiv.1802.06225" @default.
- W4293456039 hasPublicationYear "2018" @default.
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