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This course presents the principles of Reinforcement Learning as an artificial intelligence tool based on the interaction of the machine with its environment, with applications to control tasks (e.g. robotics, autonomous driving) o decision making. It also advances in the development of deep neural networks trained with little or no supervision, both for discriminative and generative tasks.
The course webpage can be found: [2020]
DLR Lectures (2020)
DLR Lectures (2020)
rl_2020_l1_Introduction.pdfLecture 1: Introduction to Reinforcement Learning
Lecture 1: Introduction to Reinforcement Learning
Learning Paradigms
Instructor: Josep Vidal
rl_2020_l2_MDP.pdfLecture 2: Markov Decission Processes and Bellman's equations
Lecture 2: Markov Decission Processes and Bellman's equations
Reinforcement Learning
Instructor: Margarita Cabrera
rl_2020_l3_DP.pdfLecture 3: Dynamic Programming
Lecture 3: Dynamic Programming
Reinforcement Learning
Instructor: Josep Vidal
rl_2020_l4_MC.pdfLecture 4: Monte Carlo Methods
Lecture 4: Monte Carlo Methods
Reinforcement Learning
Instructor: Josep Vidal
rl_2020_l5_TD.pdfLecture 5: TD-Learning: SARSA and Q-learning
Lecture 5: TD-Learning: SARSA and Q-learning
Reinforcement Learning
Instructor: Margarita Cabrera
rl_2020_l6_VFPG.pdfLecture 6: Value Function and Policy Gradient
Lecture 6: Value Function and Policy Gradient
Reinforcement Learning
Instructor: Josep Vidal
drl_2020_l1_introdeep.pdfLecture 7: Introduction to Deep Reinforcement Learning
Lecture 7: Introduction to Deep Reinforcement Learning
Learning Paradigms
Instructor: Xavi Giró-i-Nieto
drl_2020_l3_nntrain.pdfLecture 8: Training Neural Networks for RL
Lecture 8: Training Neural Networks for RL
Reinforcement Learning
Instructor: Xavi Giró-i-Nieto
drl_2020_l4_dqn.pdfLecture 9: Deep Q-Networks
Lecture 9: Deep Q-Networks
Reinforcement Learning
Instructor: Xavi Giró-i-Nieto
drl_2020_l6_reinforce.pdfLecture 10: Deep Policy Gradient
Lecture 10: Deep Policy Gradient
Reinforcement Learning
Instructor: Xavi Giró-i-Nieto
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