Reinforcement Learning

Posted in Science on October 13, 2008

Reinforcement Learning

Reinforcement learning is about learning good control policies given only weak performance feedback: occasional scalar rewards that might be delayed from the events that led to good performance. Reinforcement learning inherently deals with feedback systems rather than (data, class) data samples, providing a more flexible control-like framework than many standard machine algorithms. These lectures will summarise reinforcement learning along 3 axes:

  • Learning with or without knowledge of the system dynamics.
  • Using state values as an intermediate solution, or learning a policy directly.
  • Learning with or without fully observable system states.

Author: Douglas Aberdeen, National Ict Australia

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