Frequently Asked Questions

Q-learning learns the value of specific actions in different states—it's like memorizing which moves work best in each situation. Policy gradient methods, on the other hand, learn a direct strategy for choosing actions, which works better when you have lots of possible actions or continuous control (like steering an autonomous vehicle). For driving scenarios, policy gradients often give you smoother, more realistic vehicle behavior.

It depends on the complexity of your scenario and your simulation environment's speed. A simple intersection-crossing task might take 2-4 hours on a standard workstation, while more complex urban scenarios with multiple agents and dynamic obstacles can take 12-24 hours. The key is setting up good reward signals and starting with a well-configured simulation—most training time is actually trial-and-error tuning, not raw computation.

Not necessarily. Most reinforcement learning frameworks run fine on standard CPUs for training, though a decent GPU (like an NVIDIA RTX or similar) speeds things up significantly. What matters more is your simulation environment—if it's lightweight, a laptop works. If you're running detailed physics simulations with multiple agents, you'll want a workstation-class machine or cloud compute.

Reward design is part art, part science. You want to reward behaviors you actually want (smooth acceleration, collision avoidance, staying in lane) while penalizing dangerous ones (hard braking, crashes, rule violations). The tricky part is balancing these—if you make collision penalties too harsh, the agent gets overly cautious; too soft, it ignores safety. We typically start with a simple reward structure, test it in simulation, then iteratively refine based on what behaviors actually emerge.

Yes, that's called generalization, and it's one of the biggest challenges in this field. An agent trained only on highway driving will struggle with urban intersections. To improve generalization, you need to train on diverse scenarios, use domain randomization (vary road textures, weather, traffic patterns), and test rigorously on scenarios the agent never saw before. This is where simulation really shines—you can generate unlimited variations cheaply.

We focus on practical, research-oriented simulation rather than high-fidelity graphics. Our approach is lighter-weight, faster to iterate on, and designed specifically for reinforcement learning workflows—meaning better integration with training loops, easier scenario customization, and clearer feedback signals. If you need photorealistic rendering, commercial platforms might be better; if you need flexible, fast research cycles, our setup is usually more efficient.