Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
We present the stochastic decoupled policy gradient (SDPG), a lightweight visual reinforcement learning method that trains diverse visuomotor control policies end-to-end within a few hours on a single NVIDIA RTX 4080 GPU. SDPG estimates policy gradients via random perturbations of trajectory rollouts, requiring orders of magnitude fewer batch-rendered environments and substantially reducing compute and memory overhead. On visual MuJoCo benchmarks, SDPG consistently outperforms baseline methods in training time, memory usage, and rewards. Finally, we introduce a suite of realistic visual robotics benchmarks spanning dexterous manipulation, challenging locomotion, and demonstrate effective sim-to-real transfer on physical hardware.
Article intelligence
Key points
- SDPG enables end-to-end training of visual RL policies in hours on a single RTX 4080 GPU.
- Uses random perturbations of trajectory rollouts to estimate policy gradients, drastically reducing environment requirements.
- Outperforms baselines on visual MuJoCo benchmarks in training time, memory, and rewards.
- Introduces new realistic benchmarks for dexterous manipulation and locomotion, with successful sim-to-real transfer.
Why it matters
This matters because SDPG enables end-to-end training of visual RL policies in hours on a single RTX 4080 GPU.
Technical impact
May affect GPUs, inference clusters, compute cost, and supply-chain planning.
[2605.26478] Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
[Submitted on 26 May 2026]
Title:Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
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Abstract:We present the stochastic decoupled policy gradient (SDPG), a lightweight visual reinforcement learning (RL) method that trains diverse visuomotor control policies end-to-end within a few hours on a single NVIDIA RTX 4080 GPU. SDPG estimates policy gradients via random perturbations of trajectory rollouts, requiring orders of magnitude fewer batch-rendered environments and substantially reducing compute and memory overhead. On visual MuJoCo benchmarks, SDPG consistently outperforms baseline methods in training time, memory usage, and rewards. Finally, to support future research, we introduce a suite of realistic visual robotics benchmarks spanning dexterous manipulation, challenging locomotion, and demonstrate effective sim-to-real transfer on physical hardware.
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as: arXiv:2605.26478 [cs.RO]
(or arXiv:2605.26478v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.26478
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haoxiang You [view email] [v1] Tue, 26 May 2026 02:35:08 UTC (16,717 KB)
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