def run_task(*_):
initial_goal = np.array([0.6, -0.1, 0.80])
rospy.init_node('trpo_sim_sawyer_push_exp', anonymous=True)
push_env = PushEnv(initial_goal,
initial_joint_pos=INITIAL_ROBOT_JOINT_POS,
simulated=True)
rospy.on_shutdown(push_env.shutdown)
push_env.initialize()
env = push_env
policy = GaussianMLPPolicy(env_spec=spec(env), hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=spec(env))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=100,
discount=0.99,
step_size=0.01,
plot=False,
force_batch_sampler=True,
)
algo.train()
def run_task(*_):
"""Run task function."""
initial_goal = np.array([0.6, -0.1, 0.40])
# Initialize moveit_commander
moveit_commander.roscpp_initialize(sys.argv)
rospy.init_node('trpo_sim_sawyer_reacher_exp', anonymous=True)
env = ReacherEnv(initial_goal,
initial_joint_pos=INITIAL_ROBOT_JOINT_POS,
simulated=True)
rospy.on_shutdown(env.shutdown)
env.initialize()
policy = GaussianMLPPolicy(env_spec=spec(env), hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=spec(env))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=100,
discount=0.99,
step_size=0.01,
plot=False,
force_batch_sampler=True,
)
algo.train()
def run_pick_and_place(*_):
initial_goal = np.array([0.6, -0.1, 0.80])
env = PickAndPlaceEnv(initial_goal)
policy = GaussianMLPPolicy(env_spec=spec(env), hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=spec(env))
algo = TRPO(
env=env,
policy=policy,
batch_size=4000,
max_path_length=2000,
baseline=baseline,
n_itr=1000,
discount=0.99,
step_size=0.01,
plot=True,
force_batch_sampler=True,
)
algo.train()
def run_task(*_):
env = normalize(gym.make("Acrobot-v1"))
policy = CategoricalMLPPolicy(env_spec=spec(env), hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=spec(env))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=horizon(env),
n_itr=50,
discount=0.99,
step_size=0.01,
plot=True,
)
algo.train()
def run_block_stacking(*_):
"""Run TRPO with block stacking. """
env = BlockStackingEnv()
policy = GaussianMLPPolicy(env_spec=spec(env), hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=spec(env))
algo = TRPO(
env=env,
policy=policy,
batch_size=4000,
max_path_length=2000,
baseline=baseline,
n_itr=1000,
discount=0.99,
step_size=0.01,
plot=True,
force_batch_sampler=True,
)
algo.train()
def run_task(vv):
env = TfEnv(normalize(gym.make('HalfCheetah-v1')))
policy = GaussianMLPPolicy(env_spec=spec(env),
hidden_sizes=(32, 32),
name="policy")
baseline = LinearFeatureBaseline(env_spec=spec(env))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=40,
discount=0.99,
step_size=vv["step_size"],
# Uncomment both lines (this and the plot parameter below) to enable
# plotting
# plot=True,
)
algo.train()
from garage.baselines import LinearFeatureBaseline
from garage.envs.util import spec
from garage.misc.instrument import run_experiment
from garage.misc.instrument import stub
from garage.tf.algos import TRPO
from garage.tf.envs import TfEnv
from garage.tf.policies import CategoricalMLPPolicy
stub(globals())
# Need to wrap in a tf environment and force_reset to true
# see https://github.com/openai/rllab/issues/87#issuecomment-282519288
env = TfEnv(gym.make("CartPole-v0"))
policy = CategoricalMLPPolicy(name="policy",
env_spec=spec(env),
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=spec(env))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=200,
n_itr=120,
discount=0.99,
step_size=0.01,
)
