Reinforcement Learning

1. Introduction to Reinforcement Learning

• Definition and key concepts
  • Agents, environments, states, actions, rewards
  • Interaction loop and learning through trial and error
• Reinforcement learning vs. supervised and unsupervised learning
• Components of RL systems
• Applications overview

2. Markov Decision Processes (MDPs)

• Formal framework for decision-making
• Key elements:
  • States
  • Actions
  • Transition probabilities
  • Rewards
• Markov property
• Example MDPs

3. Value Functions and Bellman Equations

• State-value functions (V(s))
• Action-value functions (Q(s, a))
• Expected return and discount factor
• Bellman expectation and optimality equations
• Role in policy evaluation and improvement

4. Policy Iteration and Value Iteration

• Policy evaluation
• Policy improvement
• Iterative algorithms for solving MDPs
• Convergence properties

5. Q-Learning

• Model-free reinforcement learning algorithm
• Q-value updates and learning rule
• Exploration strategies (epsilon-greedy)
• Convergence and theoretical guarantees
• Practical considerations and limitations

6. Policy Gradient Methods

• Directly optimizing the policy
• REINFORCE algorithm
• Advantages over value-based methods
• Actor-Critic methods
• Techniques: advantage functions, entropy regularization

7. Deep Reinforcement Learning

• Motivation for combining deep learning with RL
• Deep Q-Networks (DQN)
• Policy gradients with neural networks
• Actor-Critic deep RL methods
• Challenges: stability, sample efficiency

8. Exploration vs. Exploitation

• The trade-off explained
• Strategies:
  • Epsilon-greedy
  • Upper Confidence Bound (UCB)
  • Thompson Sampling
• Multi-armed bandit problem as a simplified model

9. Multi-Armed Bandit Problems

• Problem formulation
• Strategies for maximizing cumulative reward
• Exploration-exploitation algorithms
• Relation to full RL problems

10. Reinforcement Learning in Practice

• Real-world applications
  • Robotics
  • Game playing
  • Recommendation systems
  • Finance
  • Healthcare
  • Autonomous driving
• Challenges in practice
• Ethical considerations
• Future directions

11. Reinforcement Learning Algorithms Comparison

• Summary of algorithms covered
• Strengths and limitations
• Use cases and choosing the right approach