What's hot in AI: Deep reinforcement learning

Deep reinforcement learning (DRL) is an exciting area of AI research, with potential applicability to a variety of problem areas. Some see DRL as a path to artificial general intelligence, or AGI, because of how it mirrors human learning by exploring and receiving feedback from environments. Recent successes of DRL agents besting human video game players, the well-publicized defeat of a Go grandmaster at the hands of DeepMind's AlphaGo, and demonstrations of bipedal agents learning to walk in simulation have all contributed to the general sense of enthusiasm about the field. Unlike supervised machine learning, which trains models based on known-correct answers, in reinforcement learning, researchers train the model by having an agent interact with an environment. When the agent's actions produce desired results, it gets positive feedback.