Deep reinforcement learning—where machines learn by testing the consequences of their actions—is one of the most promising and impactful areas of artificial intelligence. It combines deep neural networks with reinforcement learning, which together can be trained to achieve goals over many steps. It’s a crucial part of self-driving vehicles and industrial robots, which have to navigate complex environments safely and on time.
In an article for TechTalks, Pathmind CEO Chris Nicholson explains how deep reinforcement learning can help deal with complex environments that are unpredictable. Nicholson discusses the role of deep RL in creating emergent behavior, where smaller autonomous agents work independently but collectively develop systems that can handle complexity.
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