Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning

Roger Creus Castanyer, Johan Obando-Ceron, Lu Li, Pierre-Luc Bacon, Glen Berseth, Aaron Courville, Pablo Samuel Castro

Keywords: Reinforcement learning, Deep reinforcement learning, Gradients, Residuals

TL;DR: We stabilize gradients for training increasingly deep reinforcement learning agents by using a second-order optimizer and residual connections

Abstract: Scaling deep reinforcement learning networks is challenging and often results in degraded performance, yet the root causes of this failure mode remain poorly understood. Several recent works have proposed mechanisms to address this, but they are often complex and fail to highlight the causes underlying this difficulty. In this work, we conduct a series of empirical analyses which suggest that the combination of non-stationarity with gradient pathologies, due to suboptimal architectural choices, underlie the challenges of scale. We propose a series of direct interventions that stabilize gradient flow, enabling robust performance across a range of network depths and widths. Our interventions are simple to implement and compatible with well-established algorithms, and result in an effective mechanism that enables strong performance even at large scales. We validate our findings on a variety of agents and suites of environments.

Primary Area: Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics)

Submission Number: 12868