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About A Brief Survey of Deep Reinforcement Learning
Deep reinforcement learning (DRL) uses neural networks to enable reinforcement learning to solve problems with high-dimensional state and action spaces, like learning directly from visual inputs. Two major successes were developing an algorithm that learned to play Atari games at a superhuman level from pixels and AlphaGo, which defeated a human Go champion. DRL has been applied to robotics, allowing policies to be learned from camera inputs, and to developing agents that can learn to learn, or meta-learn.
Detailed Information
| Author: | ['r2d23'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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