Reinforcement Learning Game Agent

I need an end-to-end reinforcement-learning solution that can master a video game driven by on-screen visuals. The goal is to hand over a fully trained agent that reliably beats the game’s built-in AI or achieves a clearly defined performance benchmark of my choosing. Here is the scope I have in mind: • Wrap the game so it streams frames to the learning algorithm and receives control commands. I’m flexible on the control scheme—keyboard, mouse, or a combination—so use whatever allows stable training. • Select a suitable deep-RL framework (PyTorch + Stable-Baselines3, TensorFlow, RLlib, etc.) and implement best-practice techniques such as frame stacking, reward shaping, curriculum learning, or imitation pre-training if helpful. • Provide well-commented, reproducible code, a README that lets me spin up training from scratch, and the final trained weights ready to run inference in real time. • Demonstrate performance with a short recording or live demo and include logs/plots that show learning curves. I’m open to suggestions on network architecture, compute requirements, and hyper-parameter choices—just keep the focus on achieving a consistently strong in-game performance.