def test_run_continuous(self):
from tf2rl.algos.ddpg import DDPG
parser = DDPG.get_argument(self.parser)
parser.set_defaults(n_warmup=1)
args, _ = parser.parse_known_args()
def env_fn():
return gym.make('Pendulum-v0')
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1, *args, **kwargs):
return DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
n_warmup=500,
gpu=-1)
def get_weights_fn(policy):
return [policy.actor.weights,
policy.critic.weights,
policy.critic_target.weights]
def set_weights_fn(policy, weights):
actor_weights, critic_weights, critic_target_weights = weights
update_target_variables(
policy.actor.weights, actor_weights, tau=1.)
update_target_variables(
policy.critic.weights, critic_weights, tau=1.)
update_target_variables(
policy.critic_target.weights, critic_target_weights, tau=1.)
run(args, env_fn, policy_fn, get_weights_fn, set_weights_fn)
def policy_fn_continuous(env, name, memory_capacity=int(1e6), gpu=-1, *args, **kwargs):
from tf2rl.algos.ddpg import DDPG
return DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
n_warmup=500,
gpu=-1)
def setUpClass(cls):
super().setUpClass()
cls.agent = DDPG(
state_shape=cls.continuous_env.observation_space.shape,
action_dim=cls.continuous_env.action_space.low.size,
batch_size=cls.batch_size,
gpu=-1)
def _test_run_continuous(parser):
from tf2rl.algos.ddpg import DDPG
parser = DDPG.get_argument(parser)
args = parser.parse_args()
def env_fn():
return gym.make('Pendulum-v0')
sample_env = env_fn()
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1):
return DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=-1)
def get_weights_fn(policy):
return [
policy.actor.weights, policy.critic.weights,
policy.critic_target.weights
]
def set_weights_fn(policy, weights):
actor_weights, critic_weights, critic_target_weights = weights
update_target_variables(policy.actor.weights, actor_weights, tau=1.)
update_target_variables(policy.critic.weights, critic_weights, tau=1.)
update_target_variables(policy.critic_target.weights,
critic_target_weights,
tau=1.)
run(args, env_fn, policy_fn, get_weights_fn, set_weights_fn)
def test_run_continuous(self):
from tf2rl.algos.ddpg import DDPG
parser = DDPG.get_argument(self.parser)
parser.set_defaults(n_warmup=1)
args, _ = parser.parse_known_args()
run(args, env_fn_continuous, policy_fn_continuous,
get_weights_fn_continuous, set_weights_fn_continuous)
def setUpClass(cls):
super().setUpClass()
cls.agent = DDPG(
state_shape=cls.continuous_env.observation_space.shape,
action_dim=cls.continuous_env.action_space.low.size,
batch_size=cls.batch_size,
sigma=0.5, # Make noise bigger for easier to test
gpu=-1)
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1, noise_level=0.3):
return DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
max_action=env.action_space.high[0],
gpu=gpu,
name=name,
sigma=noise_level,
batch_size=100,
lr_actor=0.001,
lr_critic=0.001,
actor_units=[400, 300],
critic_units=[400, 300],
memory_capacity=memory_capacity)
def get_argument(parser=None):
"""
Create or update argument parser for command line program
Args:
parser (argparse.ArgParser, optional): argument parser
Returns:
argparse.ArgParser: argument parser
"""
parser = DDPG.get_argument(parser)
parser.add_argument('--eta', type=float, default=0.05)
return parser
def test_empty_args(self):
"""
Test empty args {}
"""
env = gym.make("Pendulum-v0")
test_env = gym.make("Pendulum-v0")
policy = DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=-1,
memory_capacity=1000,
max_action=env.action_space.high[0],
batch_size=32,
n_warmup=10)
Trainer(policy, env, {}, test_env=test_env)
def test_invalid_args(self):
"""
Test with invalid args
"""
env = gym.make("Pendulum-v0")
test_env = gym.make("Pendulum-v0")
policy = DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=-1,
memory_capacity=1000,
max_action=env.action_space.high[0],
batch_size=32,
n_warmup=10)
with self.assertRaises(ValueError):
Trainer(policy, env, {"NOT_EXISTING_OPTIONS": 1}, test_env=test_env)
def test_with_args(self):
"""
Test with args
"""
max_steps = 400
env = gym.make("Pendulum-v0")
test_env = gym.make("Pendulum-v0")
policy = DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=-1,
memory_capacity=1000,
max_action=env.action_space.high[0],
batch_size=32,
n_warmup=10)
trainer = Trainer(policy, env, {"max_steps": max_steps}, test_env=test_env)
self.assertEqual(trainer._max_steps, max_steps)
def _get_generator(self):
"""Returns instantiated policy -
parameters from ./examples/example_params/gail_params.json
"""
generator_params = self._params["ML"]["BehaviorGAILAgent"]["Generator"]
policy = DDPG(state_shape=self._environment.observation_space.shape,
action_dim=self._environment.action_space.high.size,
max_action=self._environment.action_space.high,
lr_actor=generator_params["LearningRateActor", "",
0.001],
lr_critic=generator_params["LearningRateCritic", "",
0.001],
actor_units=generator_params["ActorFcLayerParams", "",
[400, 300]],
critic_units=generator_params["CriticJointFcLayerParams",
"", [400, 300]],
sigma=generator_params["Sigma", "", 0.1],
tau=generator_params["Tau", "", 0.005],
n_warmup=generator_params["WarmUp", "", 1000],
batch_size=generator_params["BatchSize", "", 100],
gpu=self._params["ML"]["Settings"]["GPUUse", "", 0])
return policy
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1, *args, **kwargs):
return DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
n_warmup=500,
gpu=-1)
def get_argument(parser=None):
parser = DDPG.get_argument(parser)
parser.add_argument('--eta', type=float, default=0.05)
return parser
def policy_fn(env, name, memory_capacity=int(1e6), gpu=-1):
return DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=-1)
critic_units=[400, 300],
memory_capacity=memory_capacity)
def get_weights_fn(policy):
# TODO: Check if following needed
import tensorflow as tf
with tf.device(policy.device):
return [
policy.actor.weights, policy.critic.weights,
policy.critic_target.weights
]
def set_weights_fn(policy, weights):
actor_weights, critic_weights, critic_target_weights = weights
update_target_variables(policy.actor.weights, actor_weights, tau=1.)
update_target_variables(policy.critic.weights, critic_weights, tau=1.)
update_target_variables(policy.critic_target.weights,
critic_target_weights,
tau=1.)
if __name__ == '__main__':
parser = apex_argument()
parser.add_argument('--env-name', type=str, default="Pendulum-v0")
parser = DDPG.get_argument(parser)
args = parser.parse_args()
run(args, env_fn(args.env_name), policy_fn, get_weights_fn, set_weights_fn)
import gym
from tf2rl.algos.ddpg import DDPG
from tf2rl.experiments.trainer import Trainer
parser = Trainer.get_argument()
parser = DDPG.get_argument(parser)
args = parser.parse_args()
env = gym.make("Pendulum-v0")
test_env = gym.make("Pendulum-v0")
policy = DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=-1, # Run on CPU. If you want to run on GPU, specify GPU number
memory_capacity=100,
max_action=env.action_space.high[0],
batch_size=32,
n_warmup=500)
trainer = Trainer(policy, env, args, test_env=test_env)
trainer()
args = parser.parse_args()
if args.expert_path_dir is None:
print("Plaese generate demonstrations first")
print("python examples/run_sac.py --env-name=RoboschoolReacher-v1 --save-test-path --test-interval=50000")
exit()
units = [400, 300]
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
max_action=env.action_space.high[0],
gpu=args.gpu,
actor_units=units,
critic_units=units,
n_warmup=10000,
batch_size=100)
irl = VAIL(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
units=units,
enable_sn=args.enable_sn,
batch_size=32,
gpu=args.gpu)
expert_trajs = restore_latest_n_traj(
args.expert_path_dir, n_path=20, max_steps=1000)
trainer = IRLTrainer(policy, env, args, irl, expert_trajs["obses"],
expert_trajs["next_obses"], expert_trajs["acts"], test_env)
import roboschool
import gym
from tf2rl.algos.ddpg import DDPG
from tf2rl.experiments.trainer import Trainer
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = DDPG.get_argument(parser)
parser.add_argument('--env-name', type=str, default="RoboschoolAnt-v1")
parser.set_defaults(batch_size=100)
parser.set_defaults(n_warmup=10000)
args = parser.parse_args()
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = DDPG(state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=args.gpu,
memory_capacity=args.memory_capacity,
max_action=env.action_space.high[0],
batch_size=args.batch_size,
n_warmup=args.n_warmup)
trainer = Trainer(policy, env, args, test_env=test_env)
trainer()