Meet Speedy: My Dashing Journey of Creating an RL Agent to Master the CarRacing Environment

<p>Hey there, gearheads and AI enthusiasts! Buckle up because I’m going to take you on a high-octane journey today. We’re not just racing cars; we’re coding a reinforcement learning (RL) agent to race cars. Say hello to Speedy, my virtual speedster that learned to navigate the challenging CarRacing environment, powered by a DQN model and the Ray RLlib library. Let’s hit the gas and see how it all unfolded!</p> <p><iframe frameborder="0" height="480" scrolling="no" src="https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fwww.youtube.com%2Fembed%2FzJoTqSplr6Q%3Ffeature%3Doembed&display_name=YouTube&url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DzJoTqSplr6Q&image=https%3A%2F%2Fi.ytimg.com%2Fvi%2FzJoTqSplr6Q%2Fhqdefault.jpg&key=a19fcc184b9711e1b4764040d3dc5c07&type=text%2Fhtml&schema=youtube" title="Unleashing the Power of AI on the Car Racing Environment" width="854"></iframe></p> <h2>Green Light: The Challenge</h2> <p>First off, let’s talk about the circuit, the CarRacing environment. It’s a thrilling racetrack, yet intimidating for our AI buddies. It’s unpredictable, with a randomly generated track each time, chock-full of tight turns and narrow straits. The goal? Keep the car on the road and finish as quickly as possible. This is where Speedy comes in, my RL agent.</p> <p><a href="https://medium.com/@abhijeetvichare76/meet-speedy-my-dashing-journey-of-creating-an-rl-agent-to-master-the-carracing-environment-57e345ded3fc"><strong>Read More</strong></a></p>