arXiv cs.AI· Riccardo De Monte, Matteo Cederle, Gian Antonio Susto·· 14 小时前AI 评分27
从批量到流式：面向连续控制的深度强化学习新算法 S2AC 与 SDAC
Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control
AI 导读
研究提出 Streaming Soft Actor-Critic（S2AC）和 Streaming Deterministic Actor-Critic（SDAC）两种流式深度强化学习算法，可与主流批量 RL 方法兼容，适合 Sim2Real 迁移等端侧微调场景。
正文 · 原文
Abstract:State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computational complexity is often incompatible with the constraints of resource-limited hardware, due to their reliance on replay buffers, batch updates, and target networks. The emerging paradigm of streaming deep RL addresses this limitation through purely online updates, achieving strong empirical performance on standard benchmarks. In this work, we propose two novel streaming deep RL algorithms, Streaming Soft Actor-Critic (S2AC) and Streaming Deterministic Actor-Critic (SDAC), explicitly designed to be compatible with state-of-the-art batch RL methods, making them particularly suitable for on-device finetuning applications such as Sim2Real transfer. Both algorithms achieve performance comparable to state-of-the-art streaming baselines on standard benchmarks without requiring tedious per-environment hyperparameter tuning. We further investigate the batch-to-streaming transition, showing that a naive transition does not guarantee preservation of pre-trained policy performance, and propose a principled approach to address this challenge.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.08588 [cs.LG] |
| (or arXiv:2603.08588v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08588 arXiv-issued DOI via DataCite |
Submission history
From: Matteo Cederle [view email]
[v1]
Mon, 9 Mar 2026 16:40:06 UTC (7,773 KB)
[v2]
Mon, 11 May 2026 08:11:49 UTC (8,660 KB)
[v3]
Thu, 29 Oct 2026 17:11:53 UTC (8,668 KB)
来源：arXiv cs.AI · arxiv.org