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Reinforcement Learning

Course load: 80

Prerequisites

  1. Proficiency in Python.
  2. Basic machine learning knowledge.

Syllabus

Reinforcement Learning (RL). RL Algorithms. How to build a reinforcement learning solution.

Learning goals

At the end of the course, the student should be able to:

  1. Build a Reinforcement Learning system for sequential decision-making.
  2. Understand how to formalize your task as a Reinforcement Learning problem, and how to implement a solution.
  3. Understand the space of RL algorithms (Sarsa, Q-learning, Policy Gradients, and more).
  4. Understand how RL fits under the broader umbrella of machine learning, and how it complements supervised and unsupervised learning.

Detailed Syllabus

  1. Introduction to Reinforcement Learning.
  2. Implementation of autonomous agents using reinforcement learning.
  3. Temporal-Difference learning.
  4. Q-Learning algorithm.
  5. Sarsa algorithm.
  6. Policy Gradients and Proximal Policy Optimization (PPO).
  7. Deep Q-Learning algorithms.
  8. Implementations of autonomous agents using OpenAI's Gym project and Kaggle's library for RL.
  9. Reinforcement learning use cases.

Basic Bibliography

  1. GÉRON, A. Hands-on Machine Learning with Scikit-learn, Keras, and TensorFlow, 2ª ed., O'Reilly, 2021.
  2. SUTTON, R.; BARTO, A. Reinforcement Learning: An Introduction. Second Edition. The MIT Press, 2018.
  3. Van Hasselt, H., Guez, A. and Silver, D., 2016, March. Deep reinforcement learning with double q-learning. In Proceedings of the AAAI conference on artificial intelligence (Vol. 30, No. 1).
  4. Schulman, J., Wolski, F., Dhariwal, P., Radford, A. and Klimov, O., 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
  5. Brockman, G. et al., 2016. Openai gym. arXiv preprint arXiv:1606.01540.

Supplementary Bibliography

  1. NORVIG, P.; RUSSELL, S., Inteligência Artificial, 3ª ed., Campus Elsevier, 2013.
  2. SILVER, D.; SINGH S.; PRECUP D.; SUTTON R. Reward is enough. Artificial Intelligence. Vol 299, 2021.
  3. MuZero: Mastering Go, chess, shogi and Atari without rules. Publicado em Dezembro, 2020.
  4. SILVER, D.; HUBERT T.; SCHRITTWIESER, J.; ANTONOGLOU, I.; LAI, M.; GUEZ, A. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140-1144 (2018).
  5. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D. and Riedmiller, M., 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.