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