Deep Reinforcement Learning

The Deep Reinforcement Learning Nanodegree has four courses: Introduction to Deep Reinforcement Learning, Value-Based Methods, Policy-Based Methods, and Multi-Agent RL. Students learn to implement classical solution methods, define Markov decision processes, policies, and value functions, and derive Bellman equations. They learn dynamic programming, Monte Carlo methods, temporal-difference methods, deep RL, and apply these techniques to solve real-world problems. They learn to train agents to navigate virtual worlds, generate optimal financial trading strategies, and apply RL to multiple interacting agents.

• Introduction to Deep Reinforcement Learning
• Value-Based Methods
• Apply deep learning architectures to reinforcement learning tasks. Train your own agent that navigates a virtual world from sensory data.
• Policy-Based Methods
• Multi-Agent Reinforcement Learning
• Special Topics in Deep Reinforcement Learning
• Neural Networks in PyTorch
• Computing Resources
• C++ Programming
• Career Services