d3rlpy.algos.DiscreteAWR

class d3rlpy.algos.DiscreteAWR(actor_learning_rate=5e-05, critic_learning_rate=0.0001, batch_size=2048, n_frames=1, gamma=0.99, batch_size_per_update=256, n_actor_updates=1000, n_critic_updates=200, lam=0.95, beta=0.05, max_weight=20.0, momentum=0.9, use_batch_norm=False, n_epochs=1000, use_gpu=False, scaler=None, augmentation=[], n_augmentations=1, encoder_params={}, dynamics=None, impl=None, **kwargs)[source]

Discrete veriosn of Advantage-Weighted Regression algorithm.

AWR is an actor-critic algorithm that trains via supervised regression way, and has shown strong performance in online and offline settings.

The value function is trained as a supervised regression problem.

\[L(\theta) = \mathbb{E}_{s_t, R_t \sim D} [(R_t - V(s_t|\theta))^2]\]

where \(R_t\) is approximated using TD(\(\lambda\)) to mitigate high variance issue.

The policy function is also trained as a supervised regression problem.

\[J(\phi) = \mathbb{E}_{s_t, a_t, R_t \sim D} [\log \pi(a_t|s_t, \phi) \exp (\frac{1}{B} (R_t - V(s_t|\theta)))]\]

where \(B\) is a constant factor.

References

Parameters:
  • actor_learning_rate (float) – learning rate for policy function.
  • critic_learning_rate (float) – learning rate for value function.
  • batch_size (int) – batch size per iteration.
  • n_frames (int) – the number of frames to stack for image observation.
  • gamma (float) – discount factor.
  • batch_size_per_update (int) – mini-batch size.
  • n_actor_updates (int) – actor gradient steps per iteration.
  • n_critic_updates (int) – critic gradient steps per iteration.
  • lam (float) – \(\lambda\) for TD(\(\lambda\)).
  • beta (float) – \(B\) for weight scale.
  • max_weight (float) – \(w_{\text{max}}\) for weight clipping.
  • momentum (float) – momentum for stochastic gradient descent.
  • use_batch_norm (bool) – flag to insert batch normalization layers.
  • n_epochs (int) – the number of epochs to train.
  • use_gpu (bool, int or d3rlpy.gpu.Device) – flag to use GPU, device ID or device.
  • scaler (d3rlpy.preprocessing.Scaler or str) – preprocessor. The available options are [‘pixel’, ‘min_max’, ‘standard’]
  • augmentation (d3rlpy.augmentation.AugmentationPipeline or list(str)) – augmentation pipeline.
  • n_augmentations (int) – the number of data augmentations to update.
  • encoder_params (dict) – optional arguments for encoder setup. If the observation is pixel, you can pass filters with list of tuples consisting with (filter_size, kernel_size, stride) and feature_size with an integer scaler for the last linear layer size. If the observation is vector, you can pass hidden_units with list of hidden unit sizes.
  • dynamics (d3rlpy.dynamics.base.DynamicsBase) – dynamics model for data augmentation.
  • impl (d3rlpy.algos.torch.awr_impl.DiscreteAWRImpl) – algorithm implementation.
actor_learning_rate

learning rate for policy function.

Type:float
critic_learning_rate

learning rate for value function.

Type:float
batch_size

batch size per iteration.

Type:int
n_frames

the number of frames to stack for image observation.

Type:int
gamma

discount factor.

Type:float
batch_size_per_update

mini-batch size.

Type:int
n_actor_updates

actor gradient steps per iteration.

Type:int
n_critic_updates

critic gradient steps per iteration.

Type:int
lam

\(\lambda\) for TD(\(\lambda\)).

Type:float
beta

\(B\) for weight scale.

Type:float
max_weight

\(w_{\text{max}}\) for weight clipping.

Type:float
momentum

momentum for stochastic gradient descent.

Type:float
use_batch_norm

flag to insert batch normalization layers.

Type:bool
n_epochs

the number of epochs to train.

Type:int
use_gpu

GPU device.

Type:d3rlpy.gpu.Device
scaler

preprocessor.

Type:d3rlpy.preprocessing.Scaler
augmentation

augmentation pipeline.

Type:d3rlpy.augmentation.AugmentationPipeline
n_augmentations

the number of data augmentations to update.

Type:int
encoder_params

optional arguments for encoder setup.

Type:dict
dynamics

dynamics model.

Type:d3rlpy.dynamics.base.DynamicsBase
impl

algorithm implementation.

Type:d3rlpy.algos.torch.awr_impl.DiscreteAWRImpl
eval_results_

evaluation results.

Type:dict

Methods

create_impl(observation_shape, action_size)[source]

Instantiate implementation objects with the dataset shapes.

This method will be used internally when fit method is called.

Parameters:
  • observation_shape (tuple) – observation shape.
  • action_size (int) – dimension of action-space.
fit(episodes, experiment_name=None, with_timestamp=True, logdir='d3rlpy_logs', verbose=True, show_progress=True, tensorboard=True, eval_episodes=None, save_interval=1, scorers=None)

Trains with the given dataset.

algo.fit(episodes)
Parameters:
  • episodes (list(d3rlpy.dataset.Episode)) – list of episodes to train.
  • experiment_name (str) – experiment name for logging. If not passed, the directory name will be {class name}_{timestamp}.
  • with_timestamp (bool) – flag to add timestamp string to the last of directory name.
  • logdir (str) – root directory name to save logs.
  • verbose (bool) – flag to show logged information on stdout.
  • show_progress (bool) – flag to show progress bar for iterations.
  • tensorboard (bool) – flag to save logged information in tensorboard (additional to the csv data)
  • eval_episodes (list(d3rlpy.dataset.Episode)) – list of episodes to test.
  • save_interval (int) – interval to save parameters.
  • scorers (list(callable)) – list of scorer functions used with eval_episodes.
classmethod from_json(fname, use_gpu=False)

Returns algorithm configured with json file.

The Json file should be the one saved during fitting.

from d3rlpy.algos import Algo

# create algorithm with saved configuration
algo = Algo.from_json('d3rlpy_logs/<path-to-json>/params.json')

# ready to load
algo.load_model('d3rlpy_logs/<path-to-model>/model_100.pt')

# ready to predict
algo.predict(...)
Parameters:
  • fname (str) – file path to params.json.
  • use_gpu (bool, int or d3rlpy.gpu.Device) – flag to use GPU, device ID or device.
Returns:

algorithm.
Return type:

d3rlpy.base.LearnableBase
get_params(deep=True)

Returns the all attributes.

This method returns the all attributes including ones in subclasses. Some of scikit-learn utilities will use this method.

params = algo.get_params(deep=True)

# the returned values can be used to instantiate the new object.
algo2 = AlgoBase(**params)
Parameters:deep (bool) – flag to deeply copy objects such as impl.
Returns:attribute values in dictionary.
Return type:dict
load_model(fname)

Load neural network parameters.

algo.load_model('model.pt')
Parameters:fname (str) – source file path.
predict(x)

Returns greedy actions.

# 100 observations with shape of (10,)
x = np.random.random((100, 10))

actions = algo.predict(x)
# actions.shape == (100, action size) for continuous control
# actions.shape == (100,) for discrete control
Parameters:x (numpy.ndarray) – observations
Returns:greedy actions
Return type:numpy.ndarray
predict_value(x, *args, **kwargs)

Returns predicted state values.

Parameters:x (numpy.ndarray) – observations.
Returns:predicted state values.
Return type:numpy.ndarray
sample_action(x)

Returns sampled actions.

The sampled actions are identical to the output of predict method if the policy is deterministic.

Parameters:x (numpy.ndarray) – observations.
Returns:sampled actions.
Return type:numpy.ndarray
save_model(fname)

Saves neural network parameters.

algo.save_model('model.pt')
Parameters:fname (str) – destination file path.
save_policy(fname, as_onnx=False)

Save the greedy-policy computational graph as TorchScript or ONNX.

# save as TorchScript
algo.save_policy('policy.pt')

# save as ONNX
algo.save_policy('policy.onnx', as_onnx=True)

The artifacts saved with this method will work without d3rlpy. This method is especially useful to deploy the learned policy to production environments or embedding systems.

See also

Parameters:
  • fname (str) – destination file path.
  • as_onnx (bool) – flag to save as ONNX format.
set_params(**params)

Sets the given arguments to the attributes if they exist.

This method sets the given values to the attributes including ones in subclasses. If the values that don’t exist as attributes are passed, they are ignored. Some of scikit-learn utilities will use this method.

algo.set_params(n_epochs=10, batch_size=100)
Parameters:**params – arbitrary inputs to set as attributes.
Returns:itself.
Return type:d3rlpy.algos.base.AlgoBase
update(epoch, itr, batch)

Update parameters with mini-batch of data.

Parameters:
Returns:

loss values.
Return type:

list