Making Deep Q-learning methods robust to time discretization

28 Jan 2019Corentin TallecLéonard BlierYann Ollivier

Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key to making DRL applicable to real world problems... (read more)

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