Deep Reinforcement Learning

Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards. This paradigm of learning by trial-and-error, solely from rewards or punishments, is known as reinforcement learning (RL). Also like a human, our agents construct and learn their own knowledge directly from raw inputs, such as vision, without any hand-engineered features or domain heuristics. This is achieved by deep learning of neural networks. At DeepMind we have pioneered the combination of these approaches - deep reinforcement learning - to create the first artificial agents to achieve human-level performance across many challenging domains.Our agents must continually make value judgements so as to select good actions over bad. This knowledge is represented by a Q-network that estimates the total reward that an agent can expect to receive after taking a particular action. Two years ago we introducedthe first widely successful algorithm for deep reinforcement learning. The key idea was to use deep neural networks to represent the Q-network, and to train this Q-network to predict total reward.