Description:

The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents. Among the RL algorithms are TD(lambda), CMA-ES, EANT, Fitted R-Max, and Monte-Carlo learning. MMLF contains different variants of the maze-world and pole-balancing problem class as well as the mountain-car testbed.

Changes to previous version:

• Configuration files for worlds (i.e. for agents and environments) are now in the yaml syntax (and no longer in xml)
• MMLF GUI can now load and store world configuration files
• Experiments in the MMLF Experimenter can now consist of arbitrary many worlds (and not just two agents in the same world)
• MMLF Experimenter supports now parallel execution of experiments (each world in a separate process). This is based on the multiprocessing framework.
• Logging to files and providing information for the GUI is now handled both using Observables. This reduces code duplication.
• Added Actor-Critic agent
• Added Pinball Maze environment
• Added Discrete BRIO Labyrinth environment
• Added model for discrete environments which stores the transition probabilities for each (state, action, successor state) in a dict entry
• Added planner interface and refactored dynamic programming, prioritized sweeping etc. such that they implement this interface
• Added several viewers (both environment-specific as well as general purpose viewers for trajectories, value functions, policies etc.)

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Reinforcement Learning, Optimization, Evolution, Toolbox, Neuroevolution

Archive: download here

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