def test_multichar_velocityfield_x(self):
# terrainRL_PATH = os.environ['TERRAINRL_PATH']
# sys.path.append(terrainRL_PATH+'/lib')
from simAdapter import terrainRLSim
envs_list = terrainRLSim.getEnvsList()
# print ("# of envs: ", len(envs_list))
# print ("Envs:\n", json.dumps(envs_list, sort_keys=True, indent=4))
env = terrainRLSim.getEnv(
env_name="PD_Biped3D_MutliChar_WithVel_LargeBlocks-v0",
render=False)
env.reset()
actionSpace = env.getActionSpace()
env.setRandomSeed(1234)
actions = []
for i in range(11):
action = ((actionSpace.getMaximum() - actionSpace.getMinimum()) *
np.random.uniform(size=actionSpace.getMinimum().shape[0])
) + actionSpace.getMinimum()
actions.append(action)
observation, reward, done, info = env.step(actions)
states = np.array(observation)
img_data_size = 1024
agent_num = 1
data_ = []
for i in range(10):
data_.append(states[i + 1][0:img_data_size])
### There is some non-zero data
assert np.std(data_) > 0.01
plt.show()
env.finish()
def test_load_env(self):
# terrainRL_PATH = os.environ['TERRAINRL_PATH']
# sys.path.append(terrainRL_PATH+'/lib')
from simAdapter import terrainRLSim
envs_list = terrainRLSim.getEnvsList()
# print ("# of envs: ", len(envs_list))
# print ("Envs:\n", json.dumps(envs_list, sort_keys=True, indent=4))
env = terrainRLSim.getEnv(env_name="PD_Biped3D_FULL_Imitate-Steps-v0",
render=False)
env.reset()
actionSpace = env.getActionSpace()
env.setRandomSeed(1234)
env.finish()