Chapter 23: Reinforcement Learning Project
Abstract: Reinforcement learning (RL) projects involve training an agent to interact with an environment and learn optimal actions through trial and error, aiming to maximize cumulative rewards. These projects can range from simple simulations to complex real-world applications. Beginner-Friendly Projects: OpenAI Gym Environments: Solving classic control problems like CartPole, MountainCar, or LunarLander using algorithms like Q-learning or Deep Q-Networks (DQNs). Atari Games: Training an agent to play Atari games like Pong or Breakout from pixel inputs using DQNs. Custom Environments with Unity ML-Agents: Creating a simple game or simulation environment and training an RL agent to perform specific tasks within it. AWS DeepRacer: Participating in autonomous racing simulations to train a self-driving car agent. Intermediate to Advanced Projects: Robotics: Training robots to navigate mazes, perform object manipulation tasks, or learn com...