This course is all about the application of deep learning and neural networks to reinforcement learning. The combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus—they want to reach a goal. In this course, you’ll work with more complex environments, specifically, those provided by the OpenAI Gym.
- Access 52 lectures & 5 hours of content 24/7
- Extend your knowledge of temporal difference learning by looking at the TD Lambda algorithm
- Explore a special type of neural network called the RBF network
- Look at the policy gradient method
- Examine Deep Q-Learning
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