d3rlpy.dynamics.ProbabilisticEnsembleDynamics¶
- class d3rlpy.dynamics.ProbabilisticEnsembleDynamics(*, learning_rate=0.001, optim_factory=d3rlpy.models.optimizers.AdamFactory(optim_cls='Adam', betas=(0.9, 0.999), eps=1e-08, weight_decay=0.0001, amsgrad=False), encoder_factory='default', batch_size=100, n_frames=1, n_ensembles=5, variance_type='max', discrete_action=False, scaler=None, action_scaler=None, reward_scaler=None, use_gpu=False, impl=None, **kwargs)[source]¶
Probabilistic ensemble dynamics.
The ensemble dynamics model consists of \(N\) probablistic models \(\{T_{\theta_i}\}_{i=1}^N\). At each epoch, new transitions are generated via randomly picked dynamics model \(T_\theta\).
\[s_{t+1}, r_{t+1} \sim T_\theta(s_t, a_t)\]where \(s_t \sim D\) for the first step, otherwise \(s_t\) is the previous generated observation, and \(a_t \sim \pi(\cdot|s_t)\).
Note
Currently,
ProbabilisticEnsembleDynamics
only supports vector observations.References
- Parameters
learning_rate (float) – learning rate for dynamics model.
optim_factory (d3rlpy.models.optimizers.OptimizerFactory) – optimizer factory.
encoder_factory (d3rlpy.models.encoders.EncoderFactory or str) – encoder factory.
batch_size (int) – mini-batch size.
n_frames (int) – the number of frames to stack for image observation.
n_ensembles (int) – the number of dynamics model for ensemble.
variance_type (str) – variance calculation type. The available options are
['max', 'data']
.discrete_action (bool) – flag to take discrete actions.
scaler (d3rlpy.preprocessing.scalers.Scaler or str) – preprocessor. The available options are
['pixel', 'min_max', 'standard']
.action_scaler (d3rlpy.preprocessing.Actionscalers or str) – action preprocessor. The available options are
['min_max']
.reward_scaler (d3rlpy.preprocessing.RewardScaler or str) – reward preprocessor. The available options are
['clip', 'min_max', 'standard']
.use_gpu (bool or d3rlpy.gpu.Device) – flag to use GPU or device.
impl (d3rlpy.dynamics.torch.ProbabilisticEnsembleDynamicsImpl) – dynamics implementation.
kwargs (Any) –
Methods
- build_with_dataset(dataset)¶
Instantiate implementation object with MDPDataset object.
- Parameters
dataset (d3rlpy.dataset.MDPDataset) – dataset.
- Return type
- build_with_env(env)¶
Instantiate implementation object with OpenAI Gym object.
- Parameters
env (gym.core.Env) – gym-like environment.
- Return type
- create_impl(observation_shape, action_size)¶
Instantiate implementation objects with the dataset shapes.
This method will be used internally when fit method is called.
- fit(dataset, n_epochs=None, n_steps=None, n_steps_per_epoch=10000, save_metrics=True, experiment_name=None, with_timestamp=True, logdir='d3rlpy_logs', verbose=True, show_progress=True, tensorboard_dir=None, eval_episodes=None, save_interval=1, scorers=None, shuffle=True, callback=None)¶
Trains with the given dataset.
algo.fit(episodes, n_steps=1000000)
- Parameters
dataset (Union[List[d3rlpy.dataset.Episode], d3rlpy.dataset.MDPDataset]) – list of episodes to train.
n_epochs (Optional[int]) – the number of epochs to train.
n_steps (Optional[int]) – the number of steps to train.
n_steps_per_epoch (int) – the number of steps per epoch. This value will be ignored when
n_steps
isNone
.save_metrics (bool) – flag to record metrics in files. If False, the log directory is not created and the model parameters are not saved during training.
experiment_name (Optional[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_dir (Optional[str]) – directory to save logged information in tensorboard (additional to the csv data). if
None
, the directory will not be created.eval_episodes (Optional[List[d3rlpy.dataset.Episode]]) – list of episodes to test.
save_interval (int) – interval to save parameters.
scorers (Optional[Dict[str, Callable[[Any, List[d3rlpy.dataset.Episode]], float]]]) – list of scorer functions used with eval_episodes.
shuffle (bool) – flag to shuffle transitions on each epoch.
callback (Optional[Callable[[d3rlpy.base.LearnableBase, int, int], None]]) – callable function that takes
(algo, epoch, total_step)
, which is called every step.
- Returns
list of result tuples (epoch, metrics) per epoch.
- Return type
- fitter(dataset, n_epochs=None, n_steps=None, n_steps_per_epoch=10000, save_metrics=True, experiment_name=None, with_timestamp=True, logdir='d3rlpy_logs', verbose=True, show_progress=True, tensorboard_dir=None, eval_episodes=None, save_interval=1, scorers=None, shuffle=True, callback=None)¶
- Iterate over epochs steps to train with the given dataset. At each
iteration algo methods and properties can be changed or queried.
for epoch, metrics in algo.fitter(episodes): my_plot(metrics) algo.save_model(my_path)
- Parameters
dataset (Union[List[d3rlpy.dataset.Episode], d3rlpy.dataset.MDPDataset]) – list of episodes to train.
n_epochs (Optional[int]) – the number of epochs to train.
n_steps (Optional[int]) – the number of steps to train.
n_steps_per_epoch (int) – the number of steps per epoch. This value will be ignored when
n_steps
isNone
.save_metrics (bool) – flag to record metrics in files. If False, the log directory is not created and the model parameters are not saved during training.
experiment_name (Optional[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_dir (Optional[str]) – directory to save logged information in tensorboard (additional to the csv data). if
None
, the directory will not be created.eval_episodes (Optional[List[d3rlpy.dataset.Episode]]) – list of episodes to test.
save_interval (int) – interval to save parameters.
scorers (Optional[Dict[str, Callable[[Any, List[d3rlpy.dataset.Episode]], float]]]) – list of scorer functions used with eval_episodes.
shuffle (bool) – flag to shuffle transitions on each epoch.
callback (Optional[Callable[[d3rlpy.base.LearnableBase, int, int], None]]) – callable function that takes
(algo, epoch, total_step)
, which is called every step.
- Returns
iterator yielding current epoch and metrics dict.
- Return type
- 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(...)
- generate_new_data(transitions)¶
Returns generated transitions for data augmentation.
This method is for model-based RL algorithms.
- Parameters
transitions (List[d3rlpy.dataset.Transition]) – list of transitions.
- Returns
list of new transitions.
- Return type
Optional[List[d3rlpy.dataset.Transition]]
- get_action_type()[source]¶
Returns action type (continuous or discrete).
- Returns
action type.
- Return type
d3rlpy.constants.ActionSpace
- 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)
- load_model(fname)¶
Load neural network parameters.
algo.load_model('model.pt')
- predict(x, action, with_variance=False, indices=None)¶
Returns predicted observation and reward.
- Parameters
x (Union[numpy.ndarray, List[Any]]) – observation
action (Union[numpy.ndarray, List[Any]]) – action
with_variance (bool) – flag to return prediction variance.
indices (Optional[numpy.ndarray]) – index of ensemble model to return.
- Returns
tuple of predicted observation and reward. If
with_variance
isTrue
, the prediction variance will be added as the 3rd element.- Return type
Union[Tuple[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]]
- save_model(fname)¶
Saves neural network parameters.
algo.save_model('model.pt')
- save_params(logger)¶
Saves configurations as params.json.
- Parameters
logger (d3rlpy.logger.D3RLPyLogger) – logger object.
- Return type
- set_active_logger(logger)¶
Set active D3RLPyLogger object
- Parameters
logger (d3rlpy.logger.D3RLPyLogger) – logger object.
- Return type
- set_grad_step(grad_step)¶
Set total gradient step counter.
This method can be used to restart the middle of training with an arbitrary gradient step counter, which has effects on periodic functions such as the target update.
- 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(batch_size=100)
- Parameters
params (Any) – arbitrary inputs to set as attributes.
- Returns
itself.
- Return type
d3rlpy.base.LearnableBase
- update(batch)¶
Update parameters with mini-batch of data.
- Parameters
batch (d3rlpy.dataset.TransitionMiniBatch) – mini-batch data.
- Returns
dictionary of metrics.
- Return type
Attributes
- action_scaler¶
Preprocessing action scaler.
- Returns
preprocessing action scaler.
- Return type
Optional[ActionScaler]
- active_logger¶
Active D3RLPyLogger object.
This will be only available during training.
- Returns
logger object.
- grad_step¶
Total gradient step counter.
This value will keep counting after
fit
andfit_online
methods finish.- Returns
total gradient step counter.
- impl¶
Implementation object.
- Returns
implementation object.
- Return type
Optional[ImplBase]
- n_frames¶
Number of frames to stack.
This is only for image observation.
- Returns
number of frames to stack.
- Return type
- observation_shape¶
Observation shape.
- Returns
observation shape.
- Return type
Optional[Sequence[int]]
- reward_scaler¶
Preprocessing reward scaler.
- Returns
preprocessing reward scaler.
- Return type
Optional[RewardScaler]
- scaler¶
Preprocessing scaler.
- Returns
preprocessing scaler.
- Return type
Optional[Scaler]