Reinforcement Learning Fundamentals Training in Marina Bay, Singapore

The Reinforcement Learning Fundamentals Training provides a comprehensive introduction to the core concepts, techniques, and applications of reinforcement learning (RL). Participants will explore the principles of agent-based learning, reward systems, and policy optimization, gaining practical knowledge to design, implement, and evaluate RL models. This training emphasizes hands-on learning, enabling participants to apply RL concepts to real-world problems using popular frameworks and tools.

Introduction to Reinforcement Learning and Basic Algorithms

1. Introduction to Reinforcement Learning
• Overview of reinforcement learning and its applications
• Key concepts: Agent, Environment, Reward, Policy
• The Markov Decision Process (MDP)
• Practical exercise: Understanding RL environments and agents using simple examples
2. Types of Reinforcement Learning
• Model-free vs. model-based learning
• On-policy vs. off-policy learning
• Exploration vs. exploitation dilemma
• Practical exercise: Implementing an epsilon-greedy policy
3. Q-learning Algorithm
• Understanding Q-values and Q-tables
• Introduction to Bellman equations
• Implementing Q-learning in a simple grid world environment
• Practical exercise
4. Introduction to Deep Q Networks (DQN)
• Overview of Deep Q Networks and their importance in RL
• Comparing traditional Q-learning with DQN
• Practical exercise

Advanced Concepts and Practical Applications

5. SARSA Algorithm
• Understanding SARSA (State-Action-Reward-State-Action)
• Differences between Q-learning and SARSA
• Practical exercise: Implementing SARSA in a grid world environment
6. Policy Optimization
• Understanding policy iteration and value iteration
• Implementing a simple policy optimization technique
• Practical exercise: Implementing policy gradient methods
7. Applications of Reinforcement Learning
• Real-world applications of RL: Game playing, robotics, and autonomous systems
• RL in gaming: Example with OpenAI Gym
• Practical exercise
8. Challenges in Reinforcement Learning
• Delayed rewards and exploration issues
• Stability and convergence of RL algorithms
• Practical exercise
9. Course Recap and Q&A Session
• Review of key concepts learned throughout the course
• Open floor for questions and discussion
• Final thoughts and next steps for further learning