Deep Reinforcement Learning: 0 to 100

Reinforcement Learning (RL) is used to teach a virtual drone how to land on a platform through trial and error. The core concepts of RL are explained, including the agent, environment, policy, state, action, and reward system. A custom game environment is presented where the drone's state is described by 15 continuous values like position, velocity, and angle. A deep learning model serves as the policy, processing this state vector to determine the probability of firing each of the drone's three independent thrusters. The system learns by maximizing rewards and minimizing penalties based on its performance in the simulation.