Computer Science > Machine Learning
[Submitted on 9 Mar 2024 (v1), last revised 5 Aug 2024 (this version, v3)]
Title:Dissecting Deep RL with High Update Ratios: Combatting Value Divergence
View PDF HTML (experimental)Abstract:We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples by combatting value function divergence. Under large update-to-data ratios, a recent study by Nikishin et al. (2022) suggested the emergence of a primacy bias, in which agents overfit early interactions and downplay later experience, impairing their ability to learn. In this work, we investigate the phenomena leading to the primacy bias. We inspect the early stages of training that were conjectured to cause the failure to learn and find that one fundamental challenge is a long-standing acquaintance: value function divergence. Overinflated Q-values are found not only on out-of-distribution but also in-distribution data and can be linked to overestimation on unseen action prediction propelled by optimizer momentum. We employ a simple unit-ball normalization that enables learning under large update ratios, show its efficacy on the widely used dm_control suite, and obtain strong performance on the challenging dog tasks, competitive with model-based approaches. Our results question, in parts, the prior explanation for sub-optimal learning due to overfitting early data.
Submission history
From: Marcel Hussing [view email][v1] Sat, 9 Mar 2024 19:56:40 UTC (6,450 KB)
[v2] Mon, 15 Jul 2024 17:08:06 UTC (6,453 KB)
[v3] Mon, 5 Aug 2024 11:55:19 UTC (6,453 KB)
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