Title: Making Deep Q-learning methods robust to time discretization,
Abstract: 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. In this paper, we identify
sensitivity to time discretization in near continuous-time environments as a
critical factor; this covers, e.g., changing the number of frames per second,
or the action frequency of the controller. Empirically, we find that
Q-learning-based approaches such as Deep Q- learning (Mnih et al., 2015) and
Deep Deterministic Policy Gradient (Lillicrap et al., 2015) collapse with small
time steps. Formally, we prove that Q-learning does not exist in continuous
time. We detail a principled way to build an off-policy RL algorithm that
yields similar performances over a wide range of time discretizations, and
confirm this robustness empirically.