PID & Q-Learning Waypoint Follower

@richard031596

Waypoint tracking in CoppeliaSim with both a classic PID loop and a Q-learning policy — two distinct control approaches on the same wheeled robot model, likely for comparison purposes. For the PID side: standard error-based control on heading/distance to each waypoint, tuned gains (Kp/Ki/Kd) via CoppeliaSim's remote API in Python, with waypoint-switching logic once within tolerance radius. For Q-learning: discretized state space (position/heading relative to target), reward shaping around distance reduction and waypoint arrival, epsilon-greedy exploration during training, then a trained policy driving the same robot through the same waypoint set for direct comparison against PID performance. I'll be upfront: my core strength is full-stack/backend engineering and AI pipeline work (LangChain/OpenAI integrations, CNN image processing) rather than dedicated robotics/RL research, though I'm comfortable with Python control-loop implementation and have worked with reinforcement learning concepts. If you need deep robotics-specific tuning expertise (ROS integration, complex dynamics), a dedicated robotics engineer might get you there faster — happy to take this on if the scope stays at the level of two working Python control loops against your existing CoppeliaSim model.

@ganpatnayak95

Having gained significant experience in diverse digital solutions, I bring a unique perspective to your PID and Q-Learning Waypoint Follower project. Not only do I have a strong foundation on the front end and backend utilizing modern frameworks, but I have also sharpened my problem-solving abilities to handle complex challenges optimally. The combination of these skills places me at a prime position to design and implement the control loop of your python code for both PID and Q-learning. In line with the task description, my journey has involved extensively working with technologies like JavaScript and Python, which are critical components of this project. As a full stack developer, I appreciate that intelligent applications are built by seamlessly integrating every functional component for smooth user experiences and strong performance. You can expect exactly that from me - an end-to-end approach where I create resilient server-side applications, develop responsive user interfaces and go ahead to deploy the same on cloud environments. My expansive development ecosystem ranging from data management using various databases to deploying intricate systems enhances this capability. With me on board, you're not just getting someone who will complete your project; you are getting a dedicated partner invested in realizing your goals, diligently optimizing and maintaining your system.