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Basics of Reinforcement Learning


Starting from the idea of learning as the basis for intelligence, reinforcement learning (RL) has been proposed as the adaptive learning-based algorithm for decision making in animals emphasizing that the elements we encounter in this framework have empirical evidence accumulated from neuroscience studies.
This workshop is concerned with setting RL in the framework of Markov Decision Processes, learning how to obtain goal-directed behavior of an agent by means of pure interaction with an environment. It is mixed with practical examples of learning with it. It will also briefly overview some of the recent success stories concerning RL and some research challenges and frontiers in this field of research.


  1. Basics of the framework of reinforcement learning
  2. How to write experiments in python and test the learning algorithms used for goal-directed behavior of an agent in an environment.
  3. How to scale this framework to high-dimensional input and output spaces, making it more similar to how animals learn.
  4. Interact with a deep learning framework for function approximation and apply this to the same task of learning from trial and error behavior.



To be added.

sesiuni/basics-of-reinforcement-learning.1533994546.txt.gz · Last modified: 2018/08/11 16:35 by amacovei