mlpy.learners.online.rl.RLDTLearner

class mlpy.learners.online.rl.RLDTLearner(planner, max_steps=None, filename=None, profile=False)[source]

Bases: mlpy.learners.online.rl.RLLearner

Performs reinforcement learning using decision trees.

Reinforcement learning using decision trees (RL-DT) use decision trees to build the transition and reward models as described by Todd Hester and Peter Stone [R3].

Parameters:

planner : IPlanner

The planner to use to determine the best action.

max_steps : int, optional

The maximum number of steps in an iteration. Default is 100.

filename : str, optional

The name of the file to save the learner state to after each iteration. If None is given, the learner state is not saved. Default is None.

profile : bool, optional

Turn on profiling at which point profiling data is collected and saved to a text file. Default is False.

References

[R3](1, 2) Hester, Todd, and Peter Stone. “Generalized model learning for reinforcement learning in factored domains.” Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 2. International Foundation for Autonomous Agents and Multiagent Systems, 2009.

Attributes

mid The module’s unique identifier.
type This learner is of type online.

Methods

choose_action(state) Choose the next action
execute(experience) Execute learning specific updates.
learn([experience]) Learn a policy from the experience.
load(filename) Load the state of the module from file.
reset(t, **kwargs) Reset reinforcement learner.
save(filename) Save the learners state.