Deep RL Tutorial: Training a 2-DOF Robotic Arm with DQN

This tutorial explains how to train a 2-degree-of-freedom robotic arm using Deep Q-Networks, covering the Markov Decision Process framework, a physics simulation environment in Python, and a PyTorch-based DQN agent. The step-by-step guide includes code for reward shaping via Euclidean distance and epsilon-greedy action selection. It's a practical resource for developers interested in applying reinforcement learning to robotics.