Efficient Reinforcement Learning for 3D Jumping Monopods.
Sensors (Basel)
; 24(15)2024 Aug 01.
Article
en En
| MEDLINE
| ID: mdl-39124028
ABSTRACT
We consider a complex control problem:
making a monopod accurately reach a target with a single jump. The monopod can jump in any direction at different elevations of the terrain. This is a paradigm for a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimization-based techniques. Reinforcement learning (RL) is an interesting alternative, but an end-to-end approach in which the controller must learn everything from scratch can be non-trivial with a sparse-reward task like jumping. Our solution is to guide the learning process within an RL framework leveraging nature-inspired heuristic knowledge. This expedient brings widespread benefits, such as a drastic reduction of learning time, and the ability to learn and compensate for possible errors in the low-level execution of the motion. Our simulation results reveal a clear advantage of our solution against both optimization-based and end-to-end RL approaches.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Sensors (Basel)
Año:
2024
Tipo del documento:
Article
País de afiliación:
Italia
Pais de publicación:
Suiza