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arXiv Machine Learning By Juri Pfammatter, Kaixian Qu, Clemens Schwarke, Victor Klemm, Marco Hutter

Bidirectional Voronoi-biased Exploration Curriculum for Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces Bidirectional Voronoi-biased Exploration Curriculum (BVER), a method that expands start states from the goal and goals from the initial state simultaneously, guiding both toward each other to train a single goal-conditioned policy. Inspired by bidirectional RRT planning, BVER biases exploration toward unexplored task space and steers the two expansions together. Experiments on point-mass mazes, quadrupedal box climbing, and robot-arm ring-on-peg transfer show that BVER learns faster than other reference-free curricula, achieving high success rates and robustness without requiring demonstrations.

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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