A reinforcement learning policy that emits multi-step exploration episodes can replace random sampling in RRT-style planners, yielding reported order-of-magnitude speedups and higher success rates in simulated navigation.
Rapidly-exploring random trees: Progress and prospects,
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Growing Trees with an Agent: Accelerating RRTs with Learned, Multi-Step Episodic Exploration
A reinforcement learning policy that emits multi-step exploration episodes can replace random sampling in RRT-style planners, yielding reported order-of-magnitude speedups and higher success rates in simulated navigation.