9. Reinforcement Learning and Bandits
This chapter provides a comprehensive overview of Reinforcement Learning (RL) and Multi-Armed Bandits (MAB). It introduces fundamental concepts including Markov Decision Processes (MDPs), explores various algorithms such as Dynamic Programming, Monte Carlo methods, and Temporal Difference learning, and highlights the importance of exploration strategies. Applications of RL in diverse fields such as robotics, healthcare, and online recommendations are discussed, alongside contemporary challenges and future directions for research in the domain.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Reinforcement learning focuses on how agents maximize cumulative rewards through trial and error.
- Markov Decision Processes are foundational to understanding RL, involving states, actions, and policies.
- Multi-Armed Bandits represent simpler RL scenarios with a focus on exploration versus exploitation.
Key Concepts
- -- Reinforcement Learning
- A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.
- -- Markov Decision Process (MDP)
- A mathematical framework used to describe a decision-making scenario where outcomes are partly random and partly under the control of a decision maker.
- -- Exploration vs. Exploitation
- The dilemma in RL where an agent must choose between exploring new actions to find potentially better rewards or exploiting known actions that yield high rewards.
- -- Temporal Difference Learning
- A blend of Monte Carlo methods and Dynamic Programming that learns directly from raw experience without a model of the environment.
- -- Deep Reinforcement Learning
- Combines deep learning with reinforcement learning principles, allowing agents to scale up to environments with high-dimensional state spaces.
Additional Learning Materials
Supplementary resources to enhance your learning experience.