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vpg_swimmer.py
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vpg_swimmer.py
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#from rllab.algos.vpg import VPG
from sandbox.rocky.tf.algos.vpg import VPG
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.baselines.zero_baseline import ZeroBaseline
from rllab.envs.mujoco.swimmer_env import SwimmerEnv
from rllab.envs.mujoco.swimmer_randgoal_oracle_env import SwimmerRandGoalOracleEnv
from rllab.envs.mujoco.swimmer_randgoal_env import SwimmerRandGoalEnv
from rllab.envs.normalized_env import normalize
from rllab.misc.instrument import stub, run_experiment_lite
#from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
#from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.policies.minimal_gauss_mlp_policy import GaussianMLPPolicy
from sandbox.rocky.tf.envs.base import TfEnv
stub(globals())
oracle = False
random = True
if oracle:
env = TfEnv(normalize(SwimmerRandGoalOracleEnv()))
batch_size = 200
elif random:
env = TfEnv(normalize(SwimmerRandGoalEnv()))
batch_size = 200
else:
env = TfEnv(normalize(SwimmerEnv()))
batch_size = 20
policy = GaussianMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=(100,100),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
#baseline = ZeroBaseline(env_spec=env.spec)
algo = VPG(
env=env,
policy=policy,
baseline=baseline,
batch_size=500*batch_size,
max_path_length=500,
n_itr=500,
#plot=True,
optimizer_args={'tf_optimizer_args':{'learning_rate': 1e-3}},
)
run_experiment_lite(
algo.train(),
n_parallel=1, # try increasing this to make it faster??? (Maybe need to modify code for this)
snapshot_mode="last",
seed=1,
exp_prefix='vpgswimmer',
#exp_name='basic',
exp_name='randomenv',
#plot=True,
)