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Reinforcement Learning in Machine Learning: Real-World Applications
Manage episode 358963175 series 3451197
Konten disediakan oleh Michael Burke and Chris Detzel, Michael Burke, and Chris Detzel. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Michael Burke and Chris Detzel, Michael Burke, and Chris Detzel atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.
This Data Hurdles podcast episode discusses reinforcement learning in machine learning. The hosts define reinforcement learning as the process of decision making where the model learns an optimal behavior in an environment obtained by a reward. They use the analogy of a child learning how to engage with fire to explain this concept. The hosts also highlight some real-life examples of reinforcement learning being used in various fields, including gaming, robotics, marketing, healthcare, and finance.
They note that while reinforcement learning can be challenging to implement and sensitive to the choice of reward function, with careful design and tuning, it can lead to powerful and adaptable AI systems. The conversation also covers the Mario case as an interesting example of reinforcement learning in a controlled environment.
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