How We Used Reinforcement Learning to Solve the Abbey of Crime

Overview

Join us as we delve into the fascinating world of artificial intelligence and reinforcement learning with our latest exploration – conquering the iconic 1987 RPG, 'Abbey of Crime.' This session reveals how AI technologies were leveraged to autonomously navigate and master the intricate challenges presented in the game, all within a modest 120K of data. Witness a blend of nostalgic gaming and cutting-edge technology come to life, demonstrating the power and potential of AI-driven solutions.

Tune into our YouTube presentation to journey through this pioneering achievement in gaming and artificial intelligence.

Syllabus

• Introduction to the Abbey of Crime
Overview of the 1987 RPG and its complexity
Game challenges and objectives that need addressing with AI
• Fundamentals of Reinforcement Learning (RL)
Key concepts: agents, environments, states, actions, and rewards
Overview of RL algorithms used in gaming
• Analyzing the Abbey of Crime for RL Application
Mapping game elements to RL structures
Challenges in modeling game dynamics with RL
• Designing the RL Framework
Setting up the environment and state representation for Abbey of Crime
Action space definition based on game mechanics
• Implementing RL Algorithms
Selection of appropriate RL algorithms for the game
Training and tuning parameters to achieve desired outcomes
• Handling Game Constraints and Data Limitations
Strategies for efficient data use within 120k constraints
Optimization techniques for resource-constrained RL applications
• Testing and Evaluation
Methods to evaluate RL performance in-game
Iterative testing and refinement processes
• Achievements and Outcomes
Discussion on solving complex game scenarios
Insights and breakthroughs from conquering the Abbey of Crime with RL
• Lessons Learned and Future Directions
Reflections on the role of AI in gaming
Potential applications of RL across other gaming contexts
• Conclusion and Resources
Summary of key learnings from the course
Further reading and resources for advanced exploration of RL in games

---

Subjects

Conference Talks