def test_erwr_cartpole(self):
"""Test ERWR with Cartpole-v1 environment."""
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
deterministic.set_seed(1)
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
sampler = LocalSampler(
agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = ERWR(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99)
trainer.setup(algo, env)
last_avg_ret = trainer.train(n_epochs=10, batch_size=10000)
assert last_avg_ret > 60
env.close()
def erwr_cartpole(ctxt=None, seed=1):
"""Train with ERWR on CartPole-v1 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
"""
set_seed(seed)
with LocalTFRunner(snapshot_config=ctxt) as runner:
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_episode_length=100,
discount=0.99)
runner.setup(algo=algo, env=env)
runner.train(n_epochs=100, batch_size=10000, plot=False)
def run_task(*_):
"""Wrap ERWR training task in the run_task function."""
env = TfEnv(normalize(CartpoleEnv()))
policy = GaussianMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env=env,
policy=policy,
baseline=baseline,
batch_size=10000,
max_path_length=100,
n_itr=40,
discount=0.99)
algo.train()
def run_task(*_):
"""Wrap ERWR training task in the run_task function."""
env = TfEnv(env_name="CartPole-v1")
policy = CategoricalMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env=env,
policy=policy,
baseline=baseline,
batch_size=10000,
max_path_length=100,
n_itr=100,
plot=True,
discount=0.99)
algo.train()
def test_erwr_cartpole(self):
"""Test ERWR with Cartpole environment."""
logger.reset()
env = TfEnv(normalize(CartpoleEnv()))
policy = GaussianMLPPolicy(
name="policy", env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(
env=env,
policy=policy,
baseline=baseline,
batch_size=10000,
max_path_length=100,
n_itr=10,
discount=0.99)
last_avg_ret = algo.train(sess=self.sess)
assert last_avg_ret > 100
def test_erwr_cartpole(self):
"""Test ERWR with Cartpole-v1 environment."""
env = TfEnv(env_name="CartPole-v1")
policy = CategoricalMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env=env,
policy=policy,
baseline=baseline,
batch_size=10000,
max_path_length=100,
n_itr=10,
discount=0.99)
last_avg_ret = algo.train(sess=self.sess)
assert last_avg_ret > 80
env.close()
def run_task(*_):
with LocalRunner() as runner:
env = TfEnv(env_name="CartPole-v1")
policy = CategoricalMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo=algo, env=env)
runner.train(n_epochs=100, batch_size=10000, plot=True)
def run_task(snapshot_config, *_):
"""Run task."""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo=algo, env=env)
runner.train(n_epochs=100, batch_size=10000, plot=True)
def test_erwr_cartpole(self):
"""Test ERWR with Cartpole-v1 environment."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
deterministic.set_seed(1)
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99)
runner.setup(algo, env, sampler_cls=LocalSampler)
last_avg_ret = runner.train(n_epochs=10, batch_size=10000)
assert last_avg_ret > 60
env.close()
def test_erwr_cartpole(self):
"""Test ERWR with Cartpole-v1 environment."""
with LocalRunner(self.sess) as runner:
env = TfEnv(env_name="CartPole-v1")
policy = CategoricalMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=10000)
assert last_avg_ret > 80
env.close()