Reinforcement Learning (RL) has achieved groundbreaking results, from dominating video games to optimizing business processes. Despite these successes, a considerable gap exists between RL research and practical, accessible implementation for everyday developers. This talk aims to bridge that gap with hands-on guidance and real-world insights.

Based on our three-year journey of developing a production-grade RL system, we’ll cover: Translating research papers into functional code

• Avoiding common RL pitfalls
• Techniques to optimize resources without big budgets
• Real-world case studies and key lessons learned

Our experience culminated in the development of pi_optimal, an open-source library that simplifies RL implementation for Python developers. We’ll showcase how pi_optimal makes RL accessible, enabling developers to apply RL principles without needing a PhD or extensive computational resources.

What You’ll Learn

1. RL Fundamentals in Action 1.1. Core concepts explained through an interactive Super Mario AI demo 1.2. Understand RL components without diving into complex math
2. From Theory to Production 2.1. Step-by-step guide to implementing RL with pi_optimal 2.2. A real-world case study illustrating successful application 2.3. Solutions to common challenges and obstacles
3. Practical Implementation Tips 3.1. Strategies for resource optimization 3.2. Best practices for testing and debugging RL systems 3.3. Insights into deploying RL solutions in production

Live Demonstration I will show the creation and training of a simple RL agent using pi_optimal. Experience real-time learning and discover how to apply these techniques to your own projects.

Who Should Attend

• Python developers exploring AI applications
• Software engineers interested in RL systems
• Technical leaders assessing RL’s potential for their teams

Prerequisites

• Basic Python programming knowledge
• A foundational understanding of machine learning concepts
• No prior RL experience required

Key Takeaways

• A solid grasp of RL fundamentals
• Access to open-source tools and starter code with pi_optimal
• Strategies to implement RL in real-world scenarios
• Resources for deepening your RL expertise

Join our community to democratise Reinforcement Learning and empower Python developers to tackle RL challenges!