Book

Description

Deep reinforcement learning has rapidly become one of the hottest research areas in the deep learning ecosystem. The fascination with reinforcement learning is related to the fact that, from all the deep learning modalities, is the one that resemble the most how humans learn. In the last few years, no company in the world has done more to advance the stage of deep reinforcement learning than Alphabet’s subsidiary DeepMind. Since the launch of its famous AlphaGo agent, DeepMind has been at the forefront of reinforcement learning research. A few days ago, they published a new research that attempts to tackle one of the most challenging aspects of reinforcement learning solutions: multi-tasking. Since we are infants, multi-tasking becomes an intrinsic element of our cognition. The ability to performing and learning similar tasks concurrently is essential to the development of the human mind. From the neuroscientific standpoint, multi-tasking remains largely a mystery and that, not surprisingly, we have had a heck of hard time implementing artificial intelligence (AI) agents that can efficiently learn multiple domains without requiring a disproportional amount of resources. This challenge is even more evident in the case of deep reinforcement learning models that are based on trial and error exercises which can easily cross the boundaries of a single domain. Biologically speaking, you can argue that all learning is a multi-tasking exercise. This monograph introduces you with DeepMind learning.