PA230 Reinforcement Learning

Fakulta informatiky
podzim 2024
Rozsah
2/0/1. 3 kr. (plus ukončení). Ukončení: zk.
Vyučováno kontaktně
Vyučující
doc. RNDr. Petr Novotný, Ph.D. (přednášející)
Mgr. Martin Kurečka (pomocník)
Bc. Václav Nevyhoštěný (pomocník)
Bc. Vít Unčovský (pomocník)
Garance
doc. RNDr. Petr Novotný, Ph.D.
Katedra teorie programování – Fakulta informatiky
Dodavatelské pracoviště: Katedra teorie programování – Fakulta informatiky
Rozvrh
Čt 26. 9. až Čt 19. 12. Čt 14:00–15:50 B410
Předpoklady
PV021 Neural Networks
Knowledge of basic types of neural networks and of their training. Elementary knowledge of probability and statistics.
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
Cíle předmětu
The main aim of the course is to introduce the participants to the field of reinforcement learning and to acquaint them with the major approaches to training of agent policies. This will include both classical, model-based approaches, as well as modern deep-learning and tree search algorithms. The knowledge will be reinforced by a hands-on project in which the participants will train their own agents on selected benchmarks.
Výstupy z učení
After completing the course the student:
+ will have a formal understanding of the problems solved in the field of reinforcement learning (RL).
+ will be able to formulate core principles of RL algorithms.
+ will be able to describe the most prominent RL algorithms and reason about their performance characteristics and tradeoffs.
+ will have a practical experience with training of a RL agent utilizing state-of-the-art deep learning frameworks.
+ will be able to read scientificic literature from the RL domain.
Osnova
  • Aims of reinforcement learning (RL), neuropsychological connection, brief history.
  • Problem formalization: Markov decision processes, policies, payoffs.
  • Exact policy synthesis methods: value iteration, policy iteration, their relevance for RL.
  • Brief overview of tabular methods: Monte Carlo, SARSA, Q-learning. General principles: temporal difference learning, value bootstrapping.
  • Deep reinforcement learning: function approximators and issues pertaining to their use, gradient-based optimization.
  • Value-based Deep RL methods: DQN, DDQN, Rainbow heuristics, deep TD(lambda).
  • Policy gradient methods: policy gradient theorem, REINFORCE, Actor-Critic methods, SAC, trust region policy optimization (TRPO), proximal policy optimization (PPO).
  • Monte Carlo tree search (MCTS) methods: conceptual foundations (exploration vs. exploitation, multi-armed bandits, upper confidence bound), UCT-based MCTS, MCTS and deep RL (AlphaZero, MuZero, stochastic MuZero).
  • Case study: RL with human feedback in fine-tuning of large language models.
  • Some current topics in RL research: multi-agent learning (AlphaStar), safe and risk-constrained learning, hierarchical learning, transfer learning.
Literatura
  • LAPAN, Maxim. Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more. Second edition. Birmingham: Packt, 2020, xix, 798. ISBN 9781838826994. info
  • SUTTON, Richard S. a Andrew G. BARTO. Reinforcement learning : an introduction. Second edition. Cambridge, Massachusetts: The MIT Press, 2018, xxii, 526. ISBN 9780262039246. info
  • WIERING, Marco. Reinforcement learning : state of the art. Edited by Martijn van Otterlo. Berlin: Springer-Verlag, 2012, xxxiv, 638. ISBN 9783642446856. info
Výukové metody
lecture, semestral project
Metody hodnocení
semestral project, oral exam
Vyučovací jazyk
Angličtina
Informace učitele
An exception from the requirement of passing the PV021 course can be granted in some circumstances (e.g., if you enroll in PV021 in the same semester and there is enough space in PA230).
Další komentáře
Předmět je vyučován každoročně.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/fi/podzim2024/PA230