def train(num_timesteps, seed):
rank = MPI.COMM_WORLD.Get_rank()
sess = U.single_threaded_session()
sess.__enter__()
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
set_global_seeds(workerseed)
config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'configs',
'ant_navigate.yaml')
print(config_file)
env = AntNavigateEnv(config = config_file)
def policy_fn(name, ob_space, ac_space):
#return mlp_policy.MlpPolicy(name=name, ob_space=sensor_space, ac_space=ac_space, hid_size=64, num_hid_layers=2)
return cnn_policy.CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space, save_per_acts=10000, session=sess, kind='small')
env.seed(workerseed)
gym.logger.setLevel(logging.WARN)
pposgd_simple.learn(env, policy_fn,
max_timesteps=int(num_timesteps * 1.1 * 5),
timesteps_per_actorbatch=6000,
clip_param=0.2, entcoeff=0.00,
optim_epochs=4, optim_stepsize=1e-4, optim_batchsize=64,
gamma=0.99, lam=0.95,
schedule='linear',
save_per_acts=500
)
env.close()
from gibson.envs.ant_env import AntNavigateEnv, AntClimbEnv
from gibson.utils.play import play
import os
config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',
'configs', 'play', 'play_ant_nonviz.yaml')
print(config_file)
if __name__ == '__main__':
env = AntNavigateEnv(config=config_file)
play(env, zoom=4)
from gibson.envs.ant_env import AntNavigateEnv, AntClimbEnv
from gibson.utils.play import play
import os
timestep = 1.0 / (4 * 22)
frame_skip = 4
config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',
'configs', 'ant_navigate_nonviz.yaml')
print(config_file)
if __name__ == '__main__':
env = AntNavigateEnv(is_discrete=True, config=config_file)
play(env, zoom=4, fps=int(1.0 / (timestep * frame_skip)))