Project details for Maja Machine Learning Framework

Screenshot Maja Machine Learning Framework 0.9.9

by jhm - February 21, 2011, 14:05:02 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:
  • 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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