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Discrete and Continuous Action Representation for Practical RL in Video Games
[article]
2019
arXiv
pre-print
While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like those met in the video game industry is rarely studied. Operating under such constraints, we propose Hybrid SAC, an extension of the Soft Actor-Critic algorithm able to handle discrete, continuous and parameterized actions in a principled way. We show that Hybrid SAC can successfully solve a highspeed driving task in
arXiv:1912.11077v1
fatcat:znyoi6kog5fhfmoyso5f2yd6ka