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arXiv Machine Learning By Alvaro Serra-Gomez, Thomas Moerland

GEM-MPC: Balancing Exploration and Exploitation through Expert-Guided Planning

Read the original on arXiv Machine Learning →

GEM-MPC is a reinforcement learning method that blends MPPI planning with policy learning to balance exploration and exploitation in high-dimensional continuous control tasks. It trains a policy to clone the planner while also maintaining a KL-regularized policy that explores around the planner’s suggestions, thereby improving the synergy between planning and learning. The approach introduces Gated Prior Distillation, which selectively updates policies from stored planning distributions only when they offer better targets, reducing the influence of stale data without costly reanalysis. Across continuous-control benchmarks, GEM-MPC outperforms existing planning-based baselines while using lower computational budgets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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