def f_ind(n_gen, i_worker, params):
flag_model = '2D'
flag_ctrl_mode = '2D' # use 2D
seed = None
difficulty = 0
sim_dt = 0.01
sim_t = 20
timstep_limit = int(round(sim_t / sim_dt))
init_error = True
error_count = 0
while init_error:
try:
locoCtrl = OsimReflexCtrl(mode=flag_ctrl_mode, dt=sim_dt)
env = L2M2019Env(seed=seed, difficulty=difficulty, visualize=False)
env.change_model(model=flag_model,
difficulty=difficulty,
seed=seed)
obs_dict = env.reset(project=True,
seed=seed,
init_pose=init_pose,
obs_as_dict=True)
init_error = False
except Exception as e_msg:
error_count += 1
print('\ninitialization error (x{})!!!'.format(error_count))
#print(e_msg)
#import pdb; pdb.set_trace()
env.spec.timestep_limit = timstep_limit + 100
total_reward = 0
error_sim = 0
t = 0
while True:
t += sim_dt
locoCtrl.set_control_params(params)
action = locoCtrl.update(obs_dict)
obs_dict, reward, done, info = env.step(action,
project=True,
obs_as_dict=True)
total_reward += reward
if done:
break
print('\n gen#={} sim#={}: score={} time={}sec #step={}'.format(
n_gen, i_worker, total_reward, t, env.footstep['n']))
return total_reward # minimization
force_ind = []
X_list = []
y_list = []
acc_list = []
# timestep = 300
running_loss = 0.0
df = pd.DataFrame(index=range(timstep_limit), columns=['grf_r', 'grf_l'])
for i in range(timstep_limit):
t += sim_dt
# locoCtrl.set_control_params(params)
action = locoCtrl.update(obs_dict)
# done if either the pelvis of the human model falls below 0.6 meters or when it reaches 10 seconds (i=1000)
obs_dict, reward, done, info = env.step(action,
project=True,
obs_as_dict=True)
# hip_angle.append(-obs_dict['r_leg']['joint']['hip'])
# knee_angle.append(-obs_dict['r_leg']['joint']['knee'])
# ankle_angle.append(-obs_dict['r_leg']['joint']['ankle'])
# hip_angle[i, :] = -obs_dict['r_leg']['joint']['hip']
# knee_angle[i, :] = -obs_dict['r_leg']['joint']['knee']
# ankle_angle[i, :] = -obs_dict['r_leg']['joint']['ankle']
hip_abd[0, :] = -obs_dict['r_leg']['joint']['hip_abd']
hip_angle[0, :] = -obs_dict['r_leg']['joint']['hip']
knee_angle[0, :] = -obs_dict['r_leg']['joint']['knee']
A new simulation starts when `clinet.env_step` returns `done==True`
and all the simulations end when the subsequent `client.env_reset()`
returns a False
"""
mode = '3D'
difficulty = 2
visualize = False
seed = None
sim_dt = 0.01
sim_t = 10
timstep_limit = int(round(sim_t / sim_dt))
params = np.loadtxt('./osim/control/params_3D.txt')
locoCtrl = OsimReflexCtrl(mode=mode, dt=sim_dt)
total_reward = 0
while True:
locoCtrl.set_control_params(params)
action = locoCtrl.update(observation)
[observation, reward, done, info] = client.env_step(action)
total_reward += reward
print(total_reward)
if done:
total_reward = 0
observation = client.env_reset()
if not observation:
break
client.submit()