tailieunhanh - Lecture Machine learning (2014-2015) - Lecture 12: Reinforcement learning

This lecture introduces you reinforcement learning. In this chapter, students will be able to understand: Learning to act through trial and error, deep reinforcement learning, attention-based game agent, delayed rewards,. Inviting you refer. | The Promise of Reinforcement Learning Learning to act through trial and error. lEnvữonment observation reward action An agent interacts with an environment and learns by maximizing a scalar reward signal. No models labels demonstrations or any other human-provided supervision signal. Representation has been a challenge missing. Agent Volodymyr Mnih Deep Reinforcement Learning Combining deep neural networks with RL. Learn to act from high-dimensional sensory inputs. Is a noisy sparse and delayed reward signal sufficient for training deep networks Credit assignment problem. observations reward action Volodymyr Mnih

TỪ KHÓA LIÊN QUAN
crossorigin="anonymous">
Đã phát hiện trình chặn quảng cáo AdBlock
Trang web này phụ thuộc vào doanh thu từ số lần hiển thị quảng cáo để tồn tại. Vui lòng tắt trình chặn quảng cáo của bạn hoặc tạm dừng tính năng chặn quảng cáo cho trang web này.