Project details for Maja Machine Learning Framework

Screenshot Maja Machine Learning Framework 0.9.7

by jhm - May 7, 2010, 21:24:11 CET [ Project Homepage BibTeX Download ]

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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:

Added model-based learning algorithms DYNA-TD and model-based direct policy search (MBDPS). Added an initial version of a graphical user interface

BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Agnostic
Data Formats: None
Tags: Reinforcement Learning, Optimization, Evolution, Toolbox, Neuroevolution
Archive: download here

Other available revisons

Version Changelog Date
  • Experiments can now be invoked from the command line
  • Experiments can now be "scripted"
  • MMLF Experimenter contains now basic module for statistical hypothesis testing
  • MMLF Explorer can now visualize the model that has been learned by an agent
September 13, 2011, 15:13:56
  • Monitor can now also be configured in MMLF Explorer
  • MMLF Experimenter can now also load results of a prior experiment
  • MMLF Experimenter allows to specify whether world runs should be conducted sequentially or concurrently
  • Added simple LinearMarkovChain environment
  • Added "Writing an experiment" tutorial
  • Removed EANT and CGE from MMLF
April 8, 2011, 15:19:56
  • 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.)
February 21, 2011, 14:05:02
  • Added "Experimenter" GUI to MMLF that allows to compare the performance of different agents
  • Various small improvements of the GUI
  • Added a simple two-player card game environment called "Seventeen and Four"
June 4, 2010, 11:39:55

Added model-based learning algorithms DYNA-TD and model-based direct policy search (MBDPS). Added an initial version of a graphical user interface

May 7, 2010, 21:24:11

Initial Announcement on

December 4, 2009, 08:14:56


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