Have you ever wondered if it’s possible to train an AI agent to play a game using computer vision and reinforcement learning? Well, the answer is yes, and it’s actually quite fascinating.
Recently, I came across a question on Reddit where someone asked if they could use computer vision, like YOLO v8 or v11, and reinforcement learning to train an agent to play a game. The idea was to use computer vision to recognize certain elements in the game, such as when the agent kills someone, and then use reinforcement learning to train the agent to make decisions based on those observations.
What’s even more interesting is that the person asking the question didn’t want to intercept internet traffic or access the game’s memory, which makes it a more challenging but also more realistic scenario.
So, can it be done? The short answer is yes, but it requires a combination of computer vision, reinforcement learning, and possibly some natural language processing (NLP) for text recognition.
Here’s a simple pipeline to get you started:
1. Collect game footage and label the data (e.g., when the agent kills someone)
2. Train a computer vision model (e.g., YOLO) to recognize the labeled elements
3. Use reinforcement learning to train the agent to make decisions based on the recognized elements
4. Add some NLP magic to recognize text elements in the game (if needed)
Of course, this is a simplified pipeline, and the actual implementation would require more details and nuances. But the idea is to use computer vision to observe the game environment and reinforcement learning to make decisions based on those observations.
What do you think? Would you like to explore this idea further? Let me know in the comments!