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