Deep Reinforcement Learning in Action

Viac o knihe

"Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. "Deep reinforcement learning in action" teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you'll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you'll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym."-- Quatrième de couverture