Reinforcement Learning and Planning

Reinforcement Learning deals with how an agent decides on actions to take in an environment, while intending to maximise some notion of a reward. Deep neural network models now allow Reinforcement Learning methods to solve complex problems end-to-end. The module will cover decision and control theory, exploration, Q-learning and policy gradients, hierarchical Reinforcement Learning, Markov decision processes, model-based Reinforcement Learning, multi-agent Reinforcement Learning, planning and navigation.

Upon completion of the module the student will be able to:

  • summarise, implement and critically assess basic as well as state-of-the-art techniques to solve common Reinforcement Learning and Planning problems;
  • formulate and interpret the mathematical theory underlying common Reinforcement Learning and Planning approaches;
  • apply a number of devices necessary to build practical solution to Reinforcement Learning and Planning problems;
  • discuss problems and approaches at the cutting edge of Reinforcement Learning and Planning research, from an ML and AI perspective.