# 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` is `None`, 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 not `None`, 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) (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) (BaseRLModel) the trained model
classmethod `load`(load_path, env=None, **kwargs)[source]

Parameters: load_path – (str or file-like) 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. (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=1e-08, 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. (BaseRLModel) the pretrained model
`save`(save_path)[source]

Save the current parameters to file

Parameters: save_path – (str or file-like object) the save location
`set_env`(env)[source]

Checks the validity of the environment, and if it is coherent, set it as the current environment.

Parameters: env – (Gym Environment) The environment for learning a policy
`setup_model`()[source]

Create all the functions and tensorflow graphs necessary to train the model