Learnings from Multi-Armed Bandit (MAB) Course
Introduction to Multi-Armed Bandit Problems
- What is a Multi-Armed Bandit (MAB) problem?
- Real-world applications: Online advertising, recommendation systems, clinical trials
- Modelling business problems as MAB: Automating decision-making with AI agents
Reinforcement Learning & Exploration-Exploitation Tradeoff
- The Exploration-Exploitation Dilemma: Balancing between trying new options vs exploiting known rewards
- Challenges in Reinforcement Learning (RL): Sample efficiency, reward design
Algorithmic Strategies for MAB
Epsilon-Greedy Strategy
- Concept: Balancing exploration with a probability ε and exploitation otherwise
- Python Implementation: Step-by-step coding of Epsilon-Greedy
Softmax Exploration Strategy
- Concept: Selecting actions probabilistically based on their estimated values
- Python Implementation: Implementing Softmax exploration in Python
Optimistic Initialization Strategy
- Concept: Encouraging exploration by initializing high action values
- Python Implementation: Coding Optimistic Initialization for MAB
Upper Confidence Bounds (UCB) Strategy
- Concept: Choosing actions based on confidence intervals
- Python Implementation: Implementing UCB algorithm in Python
Practical Considerations in RL
- Reward Function Design: Challenges in defining effective reward structures
- Sample Efficiency: Optimizing learning with minimal data
- Incremental Sampling for Action Value Estimation: Updating action values dynamically