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High-Dimensional Robotic Reinforcement Learning with Developing Synergies

NeurIPS 2026 · Reinforcement learning › Policy gradient and actor-critic · approach: Model-based RL with guarantees

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Proposes Devyn, a lightweight framework that learns a structured low-dimensional action parameterization (synergies) during RL policy optimization for high-dimensional robotic control.

Problem

High-dimensional robotic RL is bottlenecked by inefficient exploration in large, redundant actuator spaces.

Approach

Learns a structured low-dimensional action parameterization directly during policy optimization, rather than relying on precomputed synergies or structured exploration in the full action space.

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