Reinforcement Learning in Deep Learning

Deep reinforcement learning combines deep learning with reinforcement learning, allowing agents to learn from raw input data in complex environments. Deep Q-Networks (DQN) and policy gradient methods, such as Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C), are examples of deep reinforcement learning algorithms. These techniques have been applied to various tasks, including robotics, game playing, and autonomous vehicles.