def example(*_):
env = DoublePendulumEnv()
es = OUStrategy(env_spec=env.spec)
qf = FeedForwardCritic(
name_or_scope="critic",
env_spec=env.spec,
)
policy = FeedForwardPolicy(
name_or_scope="actor",
env_spec=env.spec,
)
algorithm = DDPG(
env,
es,
policy,
qf,
n_epochs=30,
batch_size=1024,
)
algorithm.train()
def get(perm):
name = perm["problem"]
if name.lower() == "cartpole":
from rllab.envs.box2d.cartpole_env import CartpoleEnv
return normalize(CartpoleEnv())
elif name.lower() == "mountain car height bonus":
from rllab.envs.box2d.mountain_car_env import MountainCarEnv
return normalize(MountainCarEnv())
elif name.lower() == "mountain car":
from rllab.envs.box2d.mountain_car_env import MountainCarEnv
return normalize(MountainCarEnv(height_bonus=0))
elif name.lower() == "gym mountain car":
from rllab.envs.gym_env import GymEnv
return normalize(GymEnv("MountainCarContinuous-v0",
record_video=False))
elif name.lower() == "pendulum":
from rllab.envs.gym_env import GymEnv
return normalize(GymEnv("Pendulum-v0", record_video=False))
elif name.lower() == "mujoco double pendulum":
from rllab.envs.mujoco.inverted_double_pendulum_env import InvertedDoublePendulumEnv
return normalize(InvertedDoublePendulumEnv())
elif name.lower() == "double pendulum":
from rllab.envs.box2d.double_pendulum_env import DoublePendulumEnv
return normalize(DoublePendulumEnv())
elif name.lower() == "hopper":
from rllab.envs.mujoco.hopper_env import HopperEnv
return normalize(HopperEnv())
elif name.lower() == "swimmer":
from rllab.envs.mujoco.swimmer_env import SwimmerEnv
return normalize(SwimmerEnv())
elif name.lower() == "2d walker":
from rllab.envs.mujoco.walker2d_env import Walker2DEnv
return normalize(Walker2DEnv())
elif name.lower() == "half cheetah":
from rllab.envs.mujoco.half_cheetah_env import HalfCheetahEnv
return normalize(HalfCheetahEnv())
elif name.lower() == "ant":
from rllab.envs.mujoco.ant_env import AntEnv
return normalize(AntEnv())
elif name.lower() == "simple humanoid":
from rllab.envs.mujoco.simple_humanoid_env import SimpleHumanoidEnv
return normalize(SimpleHumanoidEnv())
elif name.lower() == "full humanoid":
from rllab.envs.mujoco.humanoid_env import HumanoidEnv
return normalize(HumanoidEnv())
else:
raise NotImplementedError(f"Environment {name} unknown")
from sandbox.rocky.tf.algos.robust_pg import RobustPG
from rllab.envs.box2d.double_pendulum_env import DoublePendulumEnv
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.normalized_env import normalize
from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.envs.base import TfEnv
from rllab.misc.instrument import stub, run_experiment_lite
stub(globals())
env = TfEnv(normalize(DoublePendulumEnv()))
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=(100, 50, 25),
adaptive_std=False,
# make sure log_std = 0
min_std=1,
std_parametrization='softplus',
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = RobustPG(env=env,
policy=policy,
baseline=baseline,
batch_size=10000,
max_path_length=500,
n_itr=100,
discount=0.99,