Deep reinforcement learning for physics-based musculoskeletal simulations of healthy subjects and transfemoral prostheses’ users during normal walking

HIGHLIGHTS

• who: Prostheses' Users During and colleagues from the (UNIVERSITY) have published the research work: Deep Reinforcement Learning for Physics-Based Musculoskeletal Simulations of Healthy Subjects and Transfemoral Prostheses’ Users During Normal Walking, in the Journal: (JOURNAL)
• what: The authors implement computer simulations on two musculoskeletal models, i.e., the model of a healthy human C subject and the model of a transfemoral (above-knee) amputee. Specifically, two optimization algorithms are compared, i.e., a Proximal Policy Optimization (PPO) and PPO with imitation learning , which the authors propose in this paper. This paper uses the model . . .
  

   

  

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