Deep Reinforcement Learning

Deep Q-Networks (DQNs) combine Q-learning with deep neural networks to effectively manage high-dimensional state spaces, such as images. They approximate the optimal action-value function by training neural networks, enabling agents to learn policies directly from raw sensory input. This approach has been instrumental in breakthroughs like playing Atari games at human-like levels.