Base RL Class¶
Common interface for all the RL algorithms

class
stable_baselines.common.base_class.
BaseRLModel
(policy, env, verbose=0, *, requires_vec_env, policy_base, policy_kwargs=None)[source]¶ The base RL model
Parameters:  policy – (BasePolicy) Policy object
 env – (Gym environment) The environment to learn from (if registered in Gym, can be str. Can be None for loading trained models)
 verbose – (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
 requires_vec_env – (bool) Does this model require a vectorized environment
 policy_base – (BasePolicy) the base policy used by this method

action_probability
(observation, state=None, mask=None, actions=None)[source]¶ If
actions
isNone
, then get the model’s action probability distribution from a given observation depending on the action space the output is:
 Discrete: probability for each possible action
 Box: mean and standard deviation of the action output
However if
actions
is notNone
, this function will return the probability that the given actions are taken with the given parameters (observation, state, …) on this model.Warning
When working with continuous probability distribution (e.g. Gaussian distribution for continuous action) the probability of taking a particular action is exactly zero. See http://blog.christianperone.com/2019/01/ for a good explanation
Parameters:  observation – (np.ndarray) the input observation
 state – (np.ndarray) The last states (can be None, used in recurrent policies)
 mask – (np.ndarray) The last masks (can be None, used in recurrent policies)
 actions – (np.ndarray) (OPTIONAL) For calculating the likelihood that the given actions are chosen by the model for each of the given parameters. Must have the same number of actions and observations. (set to None to return the complete action probability distribution)
Returns: (np.ndarray) the model’s action probability

get_env
()[source]¶ returns the current environment (can be None if not defined)
Returns: (Gym Environment) The current environment

learn
(total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name='run', reset_num_timesteps=True)[source]¶ Return a trained model.
Parameters:  total_timesteps – (int) The total number of samples to train on
 seed – (int) The initial seed for training, if None: keep current seed
 callback – (function (dict, dict)) > boolean function called at every steps with state of the algorithm. It takes the local and global variables. If it returns False, training is aborted.
 log_interval – (int) The number of timesteps before logging.
 tb_log_name – (str) the name of the run for tensorboard log
 reset_num_timesteps – (bool) whether or not to reset the current timestep number (used in logging)
Returns: (BaseRLModel) the trained model

classmethod
load
(load_path, env=None, **kwargs)[source]¶ Load the model from file
Parameters:  load_path – (str or filelike) the saved parameter location
 env – (Gym Envrionment) the new environment to run the loaded model on (can be None if you only need prediction from a trained model)
 kwargs – extra arguments to change the model when loading

predict
(observation, state=None, mask=None, deterministic=False)[source]¶ Get the model’s action from an observation
Parameters:  observation – (np.ndarray) the input observation
 state – (np.ndarray) The last states (can be None, used in recurrent policies)
 mask – (np.ndarray) The last masks (can be None, used in recurrent policies)
 deterministic – (bool) Whether or not to return deterministic actions.
Returns: (np.ndarray, np.ndarray) the model’s action and the next state (used in recurrent policies)

pretrain
(dataset, n_epochs=10, learning_rate=0.0001, adam_epsilon=1e08, val_interval=None)[source]¶ Pretrain a model using behavior cloning: supervised learning given an expert dataset.
NOTE: only Box and Discrete spaces are supported for now.
Parameters:  dataset – (ExpertDataset) Dataset manager
 n_epochs – (int) Number of iterations on the training set
 learning_rate – (float) Learning rate
 adam_epsilon – (float) the epsilon value for the adam optimizer
 val_interval – (int) Report training and validation losses every n epochs. By default, every 10th of the maximum number of epochs.
Returns: (BaseRLModel) the pretrained model

save
(save_path)[source]¶ Save the current parameters to file
Parameters: save_path – (str or filelike object) the save location