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_gibson_flagrun.yaml')
print(config_file)
env = AntGibsonFlagRunEnv(is_discrete=False, config = config_file)
def mlp_policy_fn(name, sensor_space, ac_space):
return mlp_policy.MlpPolicy(name=name, ob_space=sensor_space, ac_space=ac_space, hid_size=64, num_hid_layers=2)
env.seed(workerseed)
gym.logger.setLevel(logging.WARN)
pposgd_sensor.learn(env, mlp_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()
def train(num_timesteps, seed):
rank = MPI.COMM_WORLD.Get_rank()
sess = utils.make_gpu_session(args.num_gpu)
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_climb.yaml')
print(config_file)
env = AntClimbEnv(config=config_file)
env = Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), str(rank)))
env.seed(workerseed)
gym.logger.setLevel(logging.WARN)
def mlp_policy_fn(name, sensor_space, ac_space):
return mlp_policy.MlpPolicy(name=name, ob_space=sensor_space, ac_space=ac_space, hid_size=64, num_hid_layers=2)
def fuse_policy_fn(name, ob_space, sensor_space, ac_space):
return fuse_policy.FusePolicy(name=name, ob_space=ob_space, sensor_space=sensor_space, hid_size=64, num_hid_layers=2, ac_space=ac_space, save_per_acts=10000, session=sess)
if args.mode == "SENSOR":
pposgd_sensor.learn(env, mlp_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-3, optim_batchsize=64,
gamma=0.99, lam=0.95,
schedule='linear',
save_per_acts=100,
save_name="ant_ppo_mlp"
)
env.close()
else:
pposgd_fuse.learn(env, fuse_policy_fn,
max_timesteps=int(num_timesteps * 1.1),
timesteps_per_actorbatch=2000,
clip_param=0.2, entcoeff=0.01,
optim_epochs=4, optim_stepsize=LEARNING_RATE, optim_batchsize=64,
gamma=0.99, lam=0.95,
schedule='linear',
save_per_acts=50,
save_name="ant_ppo_fuse",
reload_name=args.reload_name
)
env.close()
