Making Deep Q-learning methods robust to time discretization
This addresses a critical robustness issue for applying deep reinforcement learning to real-world problems, such as varying frame rates or action frequencies, though it is incremental in improving existing methods.
The paper tackles the sensitivity of deep Q-learning methods to time discretization in near continuous-time environments, proving that Q-learning does not exist in continuous time and proposing a principled off-policy RL algorithm that achieves robust performance across various time steps.
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.