Learning to Play: History of Machine Learning in Classic Games
•Transferir como PPTX, PDF•
1 gostou•3,206 visualizações
A brief review of how AI was applied to games through the years, from the first game ever implemented to today's deep learning AlphaGo. Much of the context was in speaking, so see the accompanying blog post at: https://alteregozi.com/2016/09/17/learning-to-play/
Recomendados
Recomendados
Mais conteúdo relacionado
Destaque
Destaque (19)
Último
Último (20)
Learning to Play: History of Machine Learning in Classic Games
- 2. ARTIFICIAL INTELLIGENCE Let’s start from the basics Actually, let’s go straight to the cutting edge! Here’s a demo for a multi-layered convolutional neural network using feed-forward training to perform supervised learning
- 3. Well-defined rules A clear, measurable goa A task one can train for Bonus: human opponen
- 7. EARLY DAYS – MINIMAX Build a tree of game states (from current state) Well-defined transition rules Define a function to score each state How close are we to the goal (a winning board)? Choose path that maximizes our gain and minimizes opponent’s gains
- 8. Toc-Tac-Toe has only 765 unique states… Solved!!
- 10. 5 x 1020 (500 billion billion) possible positions
- 15. “With his passing, we lost not only a feared adversary but also a friend. Every member of our team had the deepest respect and admiration for Tinsley. It was a privilege to know him”
- 17. 10120 possible positions (checkers squared)
- 20. 160,000 positions per second Alpha-beta pruning
- 21. 200 million positions per second
- 22. GENERIC LEARNING So far, humans were central in the learning process Pre-encoding the allowed moves Providing the winning states Can machines learn on their own, like real toddlers?
- 26. 1) 33600 raw pixels 2) Target score
- 27. • We’re skipping entire ML courses now • What’s fundamentally different about Deep Learning? • No predefined rules – a generic system • “A bishop moves this way, and a knight this way…” • No domain knowledge – system “finds” the features • “count #pieces within 3 steps from the king” DEEP LEARNING
- 28. • We’re skipping even more entire ML courses now • Uses Artificial Neural Network, with LOTS of data • Deep Learning == multiple hidden layers DEEP LEARNING Magic happens HERE… Magic built-in
- 29. DEEP LEARNING IN 8-BIT
- 30. #positions > #atoms in universe 1202 CPUs and 176 GPUs GO
- 31. HOW ABOUT AI BUILDING THE GAME?...
- 32. GENERATIVE LANGUAGE MODELS • Not done with skipping ML courses just yet • First, let’s divert to literature for a bit, shall we?... • “Robert Cohn was once middleweight boxi?” https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3
- 33. GENERATIVE LANGUAGE MODELS • Feed the book into an RNN, let it train itself… 100 iterations: 1000 iterations:
- 34. GENERATIVE LANGUAGE MODELS • Feed the book into an ANN, let it train itself… <10K iterations:
- 35. GENERATIVE MODELS • Now back to games…
- 36. GENERATIVE MODELS • Extracted and encoded all game levels • Initial iterations build junk • After enough training iterations – it works!
- 37. THANK YOU!
- 38. GENERATIVE ADVERSARIAL NETWORKS (GANS)
- 39. GENERATIVE MODELS – MUSIC