garage.experiment.local_tf_runner

The local runner for TensorFlow algorithms.

A runner setup context for algorithms during initialization and pipelines data between sampler and algorithm during training.

tf = False

TFWorkerClassWrapper = False

class LocalTFRunner(snapshot_config, sess=None)

Bases: garage.experiment.LocalRunner

This class implements a local runner for TensorFlow algorithms.

A local runner provides a default TensorFlow session using python context. This is useful for those experiment components (e.g. policy) that require a TensorFlow session during construction.

Use Runner.setup(algo, env) to setup algorithm and environment for runner and Runner.train() to start training.

Parameters:

• 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.

Note

When resume via command line, new snapshots will be saved into the SAME directory if not specified.

When resume programmatically, snapshot directory should be specify manually or through @wrap_experiment interface.

Examples

# to train with LocalTFRunner() as runner:

env = gym.make(‘CartPole-v1’) policy = CategoricalMLPPolicy(

env_spec=env.spec, hidden_sizes=(32, 32))

algo = TRPO(

env=env, policy=policy, baseline=baseline, max_episode_length=100, discount=0.99, max_kl_step=0.01)

runner.setup(algo, env) runner.train(n_epochs=100, batch_size=4000)

# to resume immediately. with LocalTFRunner() as runner:

runner.restore(resume_from_dir) runner.resume()

# to resume with modified training arguments. with LocalTFRunner() as runner:

runner.restore(resume_from_dir) runner.resume(n_epochs=20)

total_env_steps

Total environment steps collected.

Returns:Total environment steps collected. Return type:int

make_sampler(self, sampler_cls, *, seed=None, n_workers=psutil.cpu_count(logical=False), max_episode_length=None, worker_class=None, sampler_args=None, worker_args=None)

Construct a Sampler from a Sampler class.

Parameters:

• sampler_cls (type) – The type of sampler to construct.
• seed (int) – Seed to use in sampler workers.
• max_episode_length (int) – Maximum episode length to be sampled by the sampler. Paths longer than this will be truncated.
• n_workers (int) – The number of workers the sampler should use.
• worker_class (type) – Type of worker the sampler should use.
• sampler_args (dict or None) – Additional arguments that should be passed to the sampler.
• worker_args (dict or None) – Additional arguments that should be passed to the worker.

Returns:

An instance of the sampler class.

Return type:

sampler_cls

setup(self, algo, env, sampler_cls=None, sampler_args=None, n_workers=psutil.cpu_count(logical=False), worker_class=None, worker_args=None)

Set up runner and sessions for algorithm and environment.

This method saves algo and env within runner and creates a sampler, and initializes all uninitialized variables in session.

Note

After setup() is called all variables in session should have been initialized. setup() respects existing values in session so policy weights can be loaded before setup().

Parameters:

• algo (RLAlgorithm) – An algorithm instance.
• env (Environment) – An environment instance.
• sampler_cls (type) – A class which implements Sampler
• sampler_args (dict) – Arguments to be passed to sampler constructor.
• n_workers (int) – The number of workers the sampler should use.
• worker_class (type) – Type of worker the sampler should use.
• worker_args (dict or None) – Additional arguments that should be passed to the worker.

initialize_tf_vars(self)

Initialize all uninitialized variables in session.

obtain_episodes(self, itr, batch_size=None, agent_update=None, env_update=None)

Obtain one batch of episodes.

Parameters:

• itr (int) – Index of iteration (epoch).
• batch_size (int) – Number of steps in batch. This is a hint that the sampler may or may not respect.
• agent_update (object) – Value which will be passed into the agent_update_fn before doing sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.
• env_update (object) – Value which will be passed into the env_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.

Raises:

ValueError – If the runner was initialized without a sampler, or batch_size wasn’t provided here or to train.

Returns:

Batch of episodes.

Return type:

EpisodeBatch

obtain_samples(self, itr, batch_size=None, agent_update=None, env_update=None)

Obtain one batch of samples.

Parameters:

• itr (int) – Index of iteration (epoch).
• batch_size (int) – Number of steps in batch. This is a hint that the sampler may or may not respect.
• agent_update (object) – Value which will be passed into the agent_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.
• env_update (object) – Value which will be passed into the env_update_fn before sampling episodes. If a list is passed in, it must have length exactly factory.n_workers, and will be spread across the workers.

Raises:

ValueError – Raised if the runner was initialized without a sampler, or batch_size wasn’t provided here or to train.

Returns:

One batch of samples.

Return type:

list[dict]

save(self, epoch)

Save snapshot of current batch.

Parameters:epoch (int) – Epoch. Raises:NotSetupError – if save() is called before the runner is set up.

restore(self, from_dir, from_epoch='last')

Restore experiment from snapshot.

Parameters:

• from_dir (str) – Directory of the pickle file to resume experiment from.
• from_epoch (str or int) – The epoch to restore from. Can be ‘first’, ‘last’ or a number. Not applicable when snapshot_mode=’last’.

Returns:

Arguments for train().

Return type:

TrainArgs

log_diagnostics(self, pause_for_plot=False)

Log diagnostics.

Parameters:pause_for_plot (bool) – Pause for plot.

train(self, n_epochs, batch_size=None, plot=False, store_episodes=False, pause_for_plot=False)

Start training.

Parameters:

• n_epochs (int) – Number of epochs.
• batch_size (int or None) – Number of environment steps in one batch.
• plot (bool) – Visualize an episode from the policy after each epoch.
• store_episodes (bool) – Save episodes in snapshot.
• pause_for_plot (bool) – Pause for plot.

Raises:

NotSetupError – If train() is called before setup().

Returns:

The average return in last epoch cycle.

Return type:

float

step_epochs(self)

Step through each epoch.

This function returns a magic generator. When iterated through, this generator automatically performs services such as snapshotting and log management. It is used inside train() in each algorithm.

The generator initializes two variables: self.step_itr and self.step_episode. To use the generator, these two have to be updated manually in each epoch, as the example shows below.

Yields:int – The next training epoch.

Examples

for epoch in runner.step_epochs():

runner.step_episode = runner.obtain_samples(…) self.train_once(…) runner.step_itr += 1

resume(self, n_epochs=None, batch_size=None, plot=None, store_episodes=None, pause_for_plot=None)

Resume from restored experiment.

This method provides the same interface as train().

If not specified, an argument will default to the saved arguments from the last call to train().

Parameters:

• n_epochs (int) – Number of epochs.
• batch_size (int) – Number of environment steps in one batch.
• plot (bool) – Visualize an episode from the policy after each epoch.
• store_episodes (bool) – Save episodes in snapshot.
• pause_for_plot (bool) – Pause for plot.

Raises:

NotSetupError – If resume() is called before restore().

Returns:

The average return in last epoch cycle.

Return type:

float

get_env_copy(self)

Get a copy of the environment.

Returns:An environment instance. Return type:Environment

LocalTFRunner