Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

Edoardo Cetin; Philip J. Ball; Steve Roberts; Oya Celiktutan

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arXiv · 2207.00986

Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

Edoardo Cetin·Philip J. Ball·Steve Roberts·Oya Celiktutan

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Abstract

Off-policy reinforcement learning (RL) from pixel observations is notoriously unstable. As a result, many successful algorithms must combine different domain-specific practices and auxiliary losses to learn meaningful behaviors in complex environments. In this work, we provide novel analysis demonstrating that these instabilities arise from performing temporal-difference learning with a convolutional encoder and low-magnitude rewards. We show that this new visual deadly triad causes unstable training and premature convergence to degenerate solutions, a phenomenon we name catastrophic self-overfitting. Based on our analysis, we propose A-LIX, a method providing adaptive regularization to the encoder's gradients that explicitly prevents the occurrence of catastrophic self-overfitting using a dual objective. By applying A-LIX, we significantly outperform the prior state-of-the-art on the DeepMind Control and Atari 100k benchmarks without any data augmentation or auxiliary losses.

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Edoardo Cetin, Philip J. Ball, Steve Roberts, Oya Celiktutan. 2022-07-03. Stabilizing Off-Policy Deep Reinforcement Learning from Pixels. https://arxiv.org/abs/2207.00986

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