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Deep Reinforcement Learning

Offered By: Nvidia Deep Learning Institute via Udacity

Tags

Deep Learning Courses C Programming Courses Reinforcement Learning Courses PyTorch Courses Markov Decision Processes Courses Dynamic programming Courses Monte Carlo Methods Courses Deep Reinforcement Learning Courses Bellman Equations Courses

Course Description

Overview

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.

Syllabus

  • 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

Taught by

Alexis Cook, Arpan Chakraborty, Mat Leonard, Luis Serrano, Cezanne Camacho, Dana Sheahan, Chhavi Yadav, Juan Delgado, Miguel Morales, Bardia H., Ross A., Camilo G., Fabian V., Ho Chit S. and Robson M.

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