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Ágnes Vathy-Fogarassy, PhD; Tibor Dulai, PhD


Topics covered

Students will learn about the following topics, including their individual training plans and interests:

T1. The basic concepts of reinforcement learning:

  • the concept and characteristics of reinforcement learning and related concepts (reward, environment, state, history)
  • the main components of reinforcement learning (policy, value function, model)
  • problem areas within reinforcement learning (learning - planning, exploration - exploitation, prediction - control, on-policy learning - off-policy learning)

T2. Markov processes and model-based solution methods:

  • Markov Reward Processes and their solutions
  • Markov Decision Processes and their solutions
  • comparing the Bellman Expectation Equation and the Bellman Optimality Equation in terms of their role and form
  • planning by dynamic programming (policy evaluation, policy iteration, and value iteration)

T3. Model-free reinforcement learning:

  • model-free prediction (Monte Carlo Learning, Temporal-Difference Learning, and TD(λ))
  • model-free control (on-policy methods: Monte Carlo Learning, on-policy Temporal-Difference Learning, and Sarsa(λ); off-policy Learning: importance sampling and Q-learning)


1.      Richard S. Sutton, Andrew G. Barto: Reinforcement learning: An introduction, The MIT Press, 2015.

2.      David Silver: RL Course by David Silver, DeepMind, 2015.