def __init__(self, visualize, integrator_accuracy, full=False, action_repeat=5, fail_reward=-0.2,
exclude_centering_frame=False):
"""
Initialize the environment:
Parameters:
- full: uses as observation vector the full observation vector
- skipFrame : How many frame to skip every action
- exclude_centering_frame: put or not the pelvis x in obs vector (obs are centered wrt pelvis x)
"""
env = L2RunEnv(visualize=visualize)
env.osim_model.set_integrator_accuracy(integrator_accuracy)
gym.Wrapper.__init__(self, env)
env.reset()
self.integrator_accuracy = integrator_accuracy
self.visualize = visualize
self.full = full
self.env = env
self.action_repeat = action_repeat
self.fail_reward = fail_reward
self.exclude_centering_frame = exclude_centering_frame
self.env_step = 0
if self.full:
self.get_observation = self.get_observation_full
else:
self.get_observation = self.get_observation_basic
self.observation_space = ( [0] * self.get_observation_space_size(), [0] * self.get_observation_space_size() )
self.observation_space = convert_to_gym(self.observation_space)
def __init__(self, visualize=True, integrator_accuracy=5e-5):
self.osim_model = OsimModel(self.model_path,
visualize,
integrator_accuracy=integrator_accuracy)
# Create specs, action and observation spaces mocks for compatibility with OpenAI gym
self.spec = Spec()
self.spec.timestep_limit = self.time_limit
if not self.action_space:
self.action_space = ([0.0] *
self.osim_model.get_action_space_size(),
[1.0] *
self.osim_model.get_action_space_size())
if not self.observation_space:
# self.observation_space = ( [-math.pi*100] * self.get_observation_space_size(), [math.pi*100] * self.get_observation_space_s
self.observation_space = ([0] * self.get_observation_space_size(),
[0] * self.get_observation_space_size())
self.action_space = convert_to_gym(self.action_space)
self.observation_space = convert_to_gym(self.observation_space)
def gymify_osim_env(env):
env.action_space = ([-1.0] * env.osim_model.get_action_space_size(),
[1.0] * env.osim_model.get_action_space_size())
env.action_space = convert_to_gym(env.action_space)
env._step = env.step
def step(self, action):
return self._step(action * 2 - 1)
env.step = types.MethodType(step, env)
return env
def make_osim_env(env):
env.action_space = ([-1.0] * env.osim_model.get_action_space_size(),
[1.0] * env.osim_model.get_action_space_size())
env.action_space = convert_to_gym(env.action_space)
env._step = env.step
def step(self, action):
#print("ACTION SPACE: {}".format(action))
return self._step(action * 2 - 1)
env.step = types.MethodType(step, env)
return env
# Examine the action space ##
action_size = env.action_space.shape[0]
print('Size of each action:', action_size)
action_low = env.action_space.low
print('Action low:', action_low)
action_high = env.action_space.high
print('Action high: ', action_high)
# Examine the state space ##
state_size = env.observation_space.shape[0]
print('Size of state:', state_size)
# Redefine action_space to -1/1 (sac implementation needs a symmetric action space) #
env.action_space = ([-1.0] * env.get_action_space_size(),
[1.0] * env.get_action_space_size())
env.action_space = convert_to_gym(env.action_space)
# Set observation space to a length of 43 (43 values are returned, but the initial environnement
# returns a observation space size of 41)
def new_get_observation_space_size():
return 43
env.get_observation_space_size = new_get_observation_space_size
env.observation_space = ([0] * env.get_observation_space_size(),
[0] * env.get_observation_space_size())
env.observation_space = convert_to_gym(env.observation_space)
# Create log dir for callback model saving
obs_body_space[:, [
20 + x for x in list(range(0, 33, 3)) + list(range(44, 77, 3))
]] = np.array([[
0, 3
]]).transpose() # (r, l) muscle forces, normalized to maximum isometric force
obs_body_space[:, [
21 + x for x in list(range(0, 33, 3)) + list(range(44, 77, 3))
]] = np.array(
[[0,
3]]).transpose() # (r, l) muscle lengths, normalized to optimal length
obs_body_space[:, [
22 + x for x in list(range(0, 33, 3)) + list(range(44, 77, 3))
]] = np.array([[-50, 50]]).transpose(
) # (r, l) muscle velocities, normalized to optimal length per second
observation_space = np.concatenate((obs_vtgt_space, obs_body_space), axis=1)
observation_space = convert_to_gym(observation_space)
def vtgt_field_to_single_vec(field):
return np.array(field)[:, 5, 5]
def obs_dict_to_list(obs_dict):
LENGTH0 = 1.
# Augmented environment from the L2R challenge
res = []
# target velocity field (in body frame)
v_tgt = np.ndarray.flatten(obs_dict['v_tgt_field'])
res += v_tgt.tolist()