Reinforcement Learning

curated by Camillo Rohe

Reinforcement Learning

It comes from

↑ Machine Learning

includes the learning paths

- Dynamic Programming

and can be specialized into

↓ Deep Reinforcement Learning

and can be specialized into

↓ Robotics

Explore / Get started with

Dynamic Programming

Follow-up paths


Start checking content

  • Gym Library

    Open source interface to reinforcement learning tasks, different environments


  • Reinforcement Learning Lectures

    Elementary Reinforcement Learning and Reinforcement Learning in Practice


  • Reinforcement learning

    Key ideas and algorithms of reinforcement learning, the field's intellectual foundations, recent developments and applications


  • Reinforcement Learning State-of-the-Art Non-fictional

    present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning


  • Reinforcement Learning: An Introduction Non-fictional

    provide a clear and simple account of the key ideas and algorithms of reinforcement learning


  • Algorithms for Reinforcement Learning

    Mathematical emphasis on RL


  • Hidden Markov Models Model

    Statistical Markov model in which the system being modeled is assumed to be a Markov process with hidden states.


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    Contact PersonCamillo Rohe
    is curator for
    Reinforcement Learning