The course is a 1.5‑ECTS hybrid course covering Reinforcement Learning from basics to state-of-the-art algorithms. Students learn to formulate and solve real-world problems using classical and modern techniques, from Q-learning to PPO and SAC, with practical focus on algorithm selection and reward learning for contemporary applications like LLM training.
Reinforcement Learning
Training details
Location
Barcelona (On-site)
Date
02/02/2026
Target Audiance
Scientist
Teaching language(s)
English
Organizing institution
BSC
Delivery mode
On-site
Level
Intermediate
Format
Hands-on session, Lecture, Tutorial, Workshop
Capacity or seats limit
30
Topics / Keywords
Reinforcement Learning, Deep Reinforcement Learning, Q-Learning, Policy Gradient Methods, DQN, Actor-Critic, PPO, Continuous Control, Policy learning.
What You Will Learn
- Understand the goals of Reinforcement Learning (RL) and the differences with Supervises Learning
- Understand characteristics of problems where RL can be applied and shine
- Learn to formulate a problem as a RL problem
- Learn basic methods for RL: Q-learning and variants
- Learn off-policy Deep RL algorithms for discrete action spaces: DQN
- On-policy Deep RL algorithms for continuous action spaces: Reinforce, Actor Critic and PPO
- Off-policy Deep RL algorithms for continuous action spaces: DDPG, TD3 and SAC
- Learn which algorithm to apply for a given problem
Agenda
- Day 1 – Introduction to RL
- Day 2 – Basic RL algorithms
- Day 3 – Deep RL
- Day 4 – Actor Critic approaches
- Day 5 – Practical RL
Instructor name(s)
Mario Martín
Instructor’s biography
Mario Martin is Associate Professor at the Polytechnic University of Catalonia (UPC). He is a member of the IDEAI-UPC Research Center and a visiting professor at the Barcelona Supercomputing Centre (BSC) since 2018. His specialization is in reinforcement learning, machine learning, and deep learning, topics where he has published more than 70 papers.
He is currently applying Multi-Agent RL techniques to marine animal tracking in collaboration with the Institute of Marine Sciences (Barcelona) and Monterey Bay Aquarium Research Institute. He is also applying RL to adaptive optics systems for ground-based Extremely Large Telescopes in collaboration with the Paris Observatory, the Australian National University, and the University of Hawaii, with technology currently deployed on the Subaru telescope in Hawaii. His recent work also includes applications of RL to robot learning.
Course Description
This course provides a comprehensive introduction to Reinforcement Learning (RL), covering both foundational concepts and state-of-the-art deep learning techniques. Students will begin by understanding the core principles of RL and how it differs fundamentally from supervised learning approaches, learning to identify problems where RL methods excel and how to properly formulate real-world challenges as RL problems.
The course progresses from classical tabular methods, including Q-learning and its variants, to modern deep reinforcement learning algorithms. Students will master off-policy methods for discrete action spaces through Deep Q-Networks (DQN), before advancing to continuous action space problems using both on-policy algorithms (REINFORCE, Actor-Critic, and Proximal Policy Optimization) and off-policy approaches (Deep Deterministic Policy Gradient, Twin Delayed DDPG, and Soft Actor-Critic).
Beyond algorithmic knowledge, the course emphasizes practical decision-making skills, teaching students how to select appropriate algorithms for specific problem characteristics.
Certificate/badge details
<p>Certificate of Achievement</p>
Technical setup
<p>Laptop with internet access; access to open‑source tools; access to Google Colab.</p>