Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning

2025-06-18

AI Summary

Deep reinforcement learning (DRL) models often suffer significant performance degradation upon scaling, a long-standing issue whose root cause remained unclear. Method: This work systematically identifies the coupling of non-stationary environment dynamics and neural network architecture as the primary source of gradient pathologies—including explosion, vanishing, and directional misalignment—that undermine scalability. To address this, we propose a lightweight, plug-and-play Gradient Flow Stabilization (GFS) framework comprising three components: gradient normalization, path-sensitive weight initialization, and temporal-aware gradient clipping—fully compatible with mainstream algorithms such as DQN and PPO. Contribution/Results: Evaluated on Atari and DeepMind Control benchmarks, GFS enables 4× depth and 3× width scaling without performance loss—yielding consistent gains. It improves training stability by 62% and accelerates convergence by 1.8×, establishing an interpretable, reusable mechanism for scalable, stable DRL.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Addresses performance degradation in scaled deep reinforcement learning
• Identifies non-stationarity and gradient pathologies as key challenges
• Proposes simple interventions to stabilize gradient flow

Innovation

• Stabilize gradient flow in deep networks
• Simple interventions compatible with algorithms
• Robust performance across network scales