Reinforcement Learning - A Gentle Introduction and Industrial Application

Overview

Explore reinforcement learning's principles and real-world application in siphonic roof drainage systems, reducing fail rates by 70% for large buildings during heavy rainfall.

Syllabus

• Introduction to Reinforcement Learning (RL)
Definition and key concepts
Difference between supervised, unsupervised, and reinforcement learning
Historical context and breakthrough moments
• Fundamentals of Reinforcement Learning
Key components: agents, environments, states, actions, and rewards
The Markov Decision Process (MDP)
Value functions: state-value and action-value functions
Policy representations and improvement
• RL Algorithms
Model-based vs. model-free methods
Dynamic programming
Monte Carlo methods
Temporal-Difference learning
Overview of Q-learning and SARSA
• Advanced RL Concepts
Deep reinforcement learning and neural networks
Policy gradient methods
Actor-critic models
Exploration vs. exploitation trade-offs
• Case Study: Siphonic Roof Drainage Systems
Introduction to siphonic drainage
Problem statement: reducing fail rates during heavy rainfall
Application of RL to optimize siphonic systems
Results analysis: achieving a 70% reduction in fail rates
• Practical Implementation
Selecting the right RL environment and tools
Setting up simulations for industrial applications
Training RL models for real-world systems
• Challenges and Future Directions
Scalability and computational limits
Ethical considerations in RL applications
Future trends and potential breakthroughs in RL technology
• Conclusion
Review of key concepts and takeaways
Discussion on the impact of RL in various industries
Final project and evaluation
• Additional Resources
Recommended readings
Online tutorials and tools
Community forums and conferences

Subjects

Conference Talks