Fade to. Red? Scheduling Coloured Noise Exploration Supports Efficient Robot Learning with Reduced Movement Jerk
Fade to... Red? Scheduling Coloured Noise Exploration Supports Efficient Robot Learning with Reduced Movement Jerk
This research investigates the use of temporally correlated coloured noise, specifically red noise, for exploration in deep reinforcement learning for continuous control. The study compares various scheduling strategies between white and red noise against generalised state-dependent exploration (gSDE) on four sparse-reward manipulation tasks using a Fetch arm and a Shadow Dexterous Hand. Results indicate that while the final noise colour does not significantly impact learning efficiency or end performance, fading towards red noise reduces movement jerk by half across all tasks compared to white noise. The authors conclude that exploration schedulers fading towards red noise are preferred for off-policy robot learning as they balance learning efficiency, end performance, and smoothness.
The authors systematically evaluated scheduling strategies for exploration noise in deep reinforcement learning, finding that fading towards red noise reduces movement jerk by approximately half while maintaining learning efficiency comparable to pink noise across four sparse-reward manipulation tasks with a Fetch arm and a Shadow Dexterous Hand.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org