DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems

@article{Schumacher2022DEPRLEE,
  title={DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems},
  author={Pierre Schumacher and Daniel F. B. Haeufle and Dieter B{\"u}chler and Syn Schmitt and Georg Martius},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.00484}
}
Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles. Reinforcement learning (RL) on large musculoskeletal models, however, has not been able to show similar performance. We conjecture that ineffective exploration in large overactuated action spaces is a key problem. This is supported by the finding that common exploration noise strategies are inadequate in synthetic examples of overactuated systems. We identify… 

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