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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.
An example of a scenario that MMLF would be used for is a robot that tries to find its way through a maze. In RL, the world is typically decomposed into the “agent(s)” and the “environment”. In the example, the robot would be the agent and the maze would be the environment.
The MMLF adopts this view since it provides a natural modularization, which allows to write general agents capable of learning and to test them in a magnitude of environments. All learning (optimization of behavior) is usually done within an agent while simulation of physics and other kinds of dynamics are performed within an environment.
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So far I got no problem using the MMLF. Its the best to know everything about it from top to bottom to ensure you’re making the most out of this tool.
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