123 points by gaming-guru 6 months ago flag hide 12 comments
johnsmith 6 months ago next
[Link to the story] (https://news.ycombinator.com/item?id=12345678) Reinforcement Learning for Game Bots: A Comprehensive Guide by johnsmith. Really enjoyed this article on HN today! If you've ever been interested in RL, Game development or bots, you'll find it fascinating. Author goes into detail on how to create game bots using RL algorithms.
doctorai 6 months ago next
@johnsmith This article is amazing. I am also a big fan of RL and the use cases mentioned here in game development is really inspiring. I have been working to build similar game bots in Unity. Will check out the approach author mentioned here.
janedoe 6 months ago next
@doctorai I have also experimented with similar game bots. I find the combination of RL and Game development very interesting. I built a small game bot for server monitoring. You can check it out here: [Link to their GitHub repo] (https://github.com/janedoe/server-monitor-bot)
coder007 6 months ago prev next
[Link to their GitHub repo] (https://github.com/coder007/game-bots-using-RL) I just put together a simple Unity project implementing game bots using RL algorithms following similar approach as mentioned in the article. Would love to get feedback from the community.
machinecat 6 months ago prev next
I've been following this field for some time now and can definitely say this article is one of the best I've read on the topic of RL for game bots. Would love to see more discussions and projects in this area.
johnsmith 6 months ago next
@machinecat Thanks for the kind words. I have put in a lot of effort to make this guide as comprehensive and easy to follow as possible so that beginners can also get started and experienced devs can find new approaches.
randomcod3r 6 months ago prev next
I have some questions about RL models used for game bots. Could someone explain how the agent decides the next action after observing the current state?
agent57 6 months ago next
@randomcod3r In RL, agents observe the current state of the game (e.g. the scores, the chess board, the cards in hand, etc.), then they evaluate all possible actions based on a policy, and the action with the maximum reward is chosen. Then, the agent observes the new state and repeats the process.
smartgo 6 months ago prev next
@randomcod3r There are various RL methods such as Q learning, SARSA, Deep Q learning, and more. The choice depends on the problem and the specific game setup. For example, Chess, Go, and Atari games have different RL approaches. It would be good different RL approaches and game setups.
curiouslearner 6 months ago prev next
How to handle game states with a massive number of possible actions? Is there a better way to deal with these complex environments instead of using brute force methods?
mathtron 6 months ago next
@curiouslearner Absolutely, when dealing with complex environments with a massive number of possible actions, brute force methods are not scalable. Instead, the state and action spaces can be discretized, and function approximation techniques like neural networks can be used. It's actually how AlphaGo beat a Go master.
deepthought 6 months ago prev next
This is such a fascinating topic, I just wish more people would work on it. The combination of game development and AI is amazing. Keep up the good work, johnsmith!