def fixture_exp(snapshot_config, sess):
"""Dummy fixture experiment function.
Args:
snapshot_config (garage.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
If None, it will create one with default settings.
sess (tf.Session): An optional TensorFlow session.
A new session will be created immediately if not provided.
Returns:
np.ndarray: Values of the parameters evaluated in
the current session
"""
with LocalTFRunner(snapshot_config=snapshot_config, sess=sess) as runner:
env = GarageEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(8, 8))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_episode_length=100,
discount=0.99,
optimizer_args=dict(learning_rate=0.01, ))
runner.setup(algo, env, sampler_cls=LocalSampler)
runner.train(n_epochs=5, batch_size=100)
return policy.get_param_values()
def fixture_exp():
with LocalRunner() as runner:
env = TfEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(
name='policy', env_spec=env.spec, hidden_sizes=(8, 8))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = VPG(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
optimizer_args=dict(tf_optimizer_args=dict(learning_rate=0.01, )))
runner.setup(algo, env)
runner.train(n_epochs=5, batch_size=100)
return policy.get_param_values()