Module Expr Str Assign NameStore Str ImportFrom alias Import alias ClassDef bases NameLoad body Expr Str Assign NameStore NameLoad FunctionDef arguments args NameParam defaults body Expr Str Expr Call NameLoad decorator_list decorator_list ClassDef bases NameLoad body Expr Str Assign NameStore NameLoad FunctionDef arguments args NameParam NameParam NameParam defaults body If CompareIsNot AttributeLoad NameLoad attr NameLoad body Return Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad NameLoad orelse Return NameLoad decorator_list decorator_list ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam defaults Num vararg kwarg body Expr Str For NameStore Call NameLoad NameLoad body Expr Call AttributeLoad NameLoad attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam defaults body Expr Str If CompareIsNot AttributeLoad NameLoad attr NameLoad body Expr Call AttributeLoad AttributeLoad NameLoad attr attr decorator_list FunctionDef arguments args NameParam defaults body Pass decorator_list decorator_list ClassDef bases NameLoad body Expr Str Assign NameStore NameLoad FunctionDef arguments args NameParam NameParam defaults vararg kwarg body Expr Str Assign AttributeStore NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad decorator_list decorator_list
N/A N/A  The top of the learner hierarchy is more conceptual than functional.
The different classes distinguish algorithms in such a way that we can automatically
determine when an algorithm is not applicable for a problem.  N/A __author__ Tom Schaul, tom@idsia.ch, Thomas Rueckstiess, ruecksti@in.tum.de pybrain.utilities abstractMethod N/A logging Learner N/A object N/A N/A  Top-level class for all reinforcement learning algorithms.
    Any learning algorithm changes a policy (in some way) in order
    to increase the expected reward/fitness.
     N/A module None learn N/A N/A self N/A N/A N/A  The main method, that invokes a learning step.  N/A N/A abstractMethod N/A N/A ExploringLearner N/A Learner N/A N/A  A Learner determines how to change the adaptive parameters of a module.
     N/A explorer None explore N/A N/A self state action N/A N/A N/A N/A N/A self explorer None N/A N/A N/A N/A N/A self explorer activate state action N/A N/A action N/A N/A EpisodicLearner N/A Learner N/A N/A  Assumes the task is episodic, not life-long,
    and therefore does a learning step only after the end of each episode.  learnEpisodes N/A N/A self episodes N/A 1 args kwargs N/A N/A  learn on the current dataset, for a number of episodes  N/A _ N/A range episodes N/A N/A N/A N/A self learn args kwargs N/A newEpisode N/A N/A self N/A N/A N/A  informs the learner that a new episode has started.  N/A N/A N/A self explorer None N/A N/A N/A N/A N/A self explorer newEpisode N/A reset N/A N/A self N/A N/A N/A N/A N/A DataSetLearner N/A EpisodicLearner N/A N/A  A class for learners that learn from a dataset, which has no target output but
        only a reinforcement signal for each sample. It requires a
        ReinforcementDataSet object (which provides state-action-reward tuples).  N/A dataset None learnOnDataset N/A N/A self dataset N/A args kwargs N/A N/A  set the dataset, and learn  N/A N/A self dataset dataset N/A N/A N/A self learnEpisodes args kwargs N/A N/A