Deep Reinforcement Learning is pivotal for teaching machines to make sequences of decisions. By integrating the perception capabilities of deep learning (where neural networks can interpret complex inputs such as images or speech) with the goal-oriented approach of reinforcement learning (which focuses on maximizing an agent's reward in an environment), DRL allows AI systems to learn from direct interaction with their environment without needing predefined labels. This approach has been fundamental in achieving state-of-the-art performances in complex tasks such as playing video games at superhuman levels, autonomous driving, robotic manipulation, and strategic game playing like Go or Chess. It underscores the capacity of AI to learn policies that map observations to actions, aiming to achieve long-term objectives, and it highlights the significance of exploration and exploitation in learning.
Historical overview: The concept of combining deep learning with reinforcement learning gained prominence in the early 2010s, with a landmark paper published by DeepMind in 2013 demonstrating a DRL system that could learn to play Atari video games directly from pixel inputs.
Key contributors: Significant figures in the development of DRL include Volodymyr Mnih, who was among the authors of the seminal 2013 DeepMind paper that popularized the DQN algorithm (Deep Q-Networks), and other researchers at DeepMind like David Silver, instrumental in the development of AlphaGo. Their work laid the foundation for numerous advancements in the field of deep reinforcement learning.