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Interface

In order to create an environment, use:

from osim.env import L2RunEnv

env = L2RunEnv(visualize = True)

Parameters:

  • visualize - turn the visualizer on and off

Methods of L2RunEnv

reset(difficulty = 2, seed = None, project = True)

Restart the enivironment with a given difficulty level and a seed.

  • difficulty - 0 - no obstacles, 1 - 3 randomly positioned obstacles (balls fixed in the ground), 2 - same as 1 but also strength of the psoas muscles (the muscles that help bend the hip joint in the model) varies. The muscle strength is set to z * 100%, where z is a normal variable with the mean 1 and the standard deviation 0.1
  • seed - starting seed for the random number generator. If the seed is None, generation from the previous seed is continued.

Your solution will be graded in the environment with difficulty = 2, yet it might be easier to train your model with difficulty = 0 first and then retrain with a higher difficulty

Returns

  • observation - a vector (if project = True) or a dictionary describing the state of muscles, joints, and bodies in the biomechanical system.

step(action, project = True)

Make one iteration of the simulation.

  • action - a list of length 18 of continuous values in [0,1] corresponding to excitation of muscles.

The function returns:

  • observation - a vector (if project = True) or a dictionary describing the state of muscles, joints, and bodies in the biomechanical system. Note that only project = True is consistent with the actual NIPS 2017 challenge.

  • reward - reward gained in the last iteration. The reward is computed as a change in position of the pelvis along the x axis minus the penalty for the use of ligaments. See the “Physics of the model” section for details.

  • done - indicates if the move was the last step of the environment. This happens if either 1000 iterations were reached or the pelvis height is below 0.65 meters.

  • info - for compatibility with OpenAI, currently not used.