Machine Learning contents are recommended for the Reinforcement Learning course

Introduction to Reinforcement Learning - Introduction to reinforcement learning - Markov Decision Processes - Policies and optimality: discounted infinite horizon - Value Functions Dynamic Programming - Problem Solving on MDP: model-free, model-based and dynamic programming methods - Policy Iteration Algorithm - Value Iteration Algorithm Direct reinforcement learning - Monte Carlo methods: and Monte Carlo with exploratory start - Model-free methods: Q-Learning - Example of execution of Q-Learning - On-policy methods vs. off-policy: SARSA - Exploration and exploitation: e-greedy and softmax Model-Based Methods - Model Learning - Dyna-Q Representation in Reinforcement Learning - Representation of the space of states, actions and Q - State space discretization: uniform and adaptive methods - Approximate methods to represent the function Q: Batch Q-Learning Generalization Through Function Approximation - Approximation through neural networks - Deep reinforcement learning Policy Search Methods - Policy Approximation - Actor-critic methods - Proximal Policy Optimization (PPO) Other Reinforcement Learning topics - Hierarchical Reinforcement Learning - Transfer of learning learned - Multi-agent Reinforcement Learning - Safe reinforcement learning - Offline Reinforcement Learning - Multi-objective Reinforcement Learning - Partially observable Reinforcement Learning Reinforcement Learning in the real world: - Applications of reinforcement learning - Reinforcement learning frameworks and software

Training Activities: -------------------------- AF1 - Theoretical class AF3 - Theoretical-practical classes AF5 - Individual and group tutorials AF6 - Group work AF7 - Individual student work Teaching methodology: ------------------------ MD1: Class lectures by the lecturer with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the students' learning. MD2: Critical reading of texts recommended by the subject teacher: press articles, reports, manuals and/or academic articles, either for subsequent discussion in class or to expand and consolidate knowledge of the subject. MD3: Resolution of practical cases, problems, etc... posed by the teacher individually or in groups. MD4: Presentation and discussion in class, under the moderation of the teacher, of topics related to the content of the subject, as well as practical cases. MD5: Preparation of individual or group work and reports.