Deep Reinforcement Learning Nanodegree
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Deep Reinforcement Learning Nanodegree

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udacity公众号

Nanodegree key: nd893
Version: 6.0.0
Locale: en-us
This course trains the learner to master the deep reinforcement learning skills that are powering amazing advances in AI.

Content

Part 01 : Introduction to Deep Reinforcement Learning

Module 01: Introduction to Deep Reinforcement Learning
- Lesson 01: Welcome to Deep Reinforcement LearningWelcome to the Deep Reinforcement Learning Nanodegree program!
- Lesson 02: Knowledge, Community, and CareersYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 03: Get Help with Your AccountWhat to do if you have questions about your account or general questions about the program.
  - Concept 01: FAQ
  - Concept 02: Support
- Lesson 04: Learning PlanObtain helpful resources to accelerate your learning in this first part of the Nanodegree program.
- Lesson 05: Introduction to RLReinforcement learning is a type of machine learning where the machine or software agent learns how to maximize its performance at a task.
- Lesson 06: The RL Framework: The ProblemLearn how to mathematically formulate tasks as Markov Decision Processes.
- Lesson 07: The RL Framework: The SolutionIn reinforcement learning, agents learn to prioritize different decisions based on the rewards and punishments associated with different outcomes.
- Lesson 08: Monte Carlo MethodsWrite your own implementation of Monte Carlo control to teach an agent to play Blackjack!
- Lesson 09: Temporal-Difference MethodsLearn about how to apply temporal-difference methods such as SARSA, Q-Learning, and Expected SARSA to solve both episodic and continuing tasks.
- Lesson 10: Solve OpenAI Gym’s Taxi-v2 TaskWith reinforcement learning now in your toolbox, you’re ready to explore a mini project using OpenAI Gym!
- Lesson 11: RL in Continuous SpacesLearn how to adapt traditional algorithms to work with continuous spaces.
- Lesson 12: What’s Next?In the next parts of the Nanodegree program, you’ll learn all about how to use neural networks as powerful function approximators in reinforcement learning.
  - Concept 01: Congratulations!
  - Concept 02: What can you do now?
    Part 02 : Value-Based Methods
Module 01: Value-Based Methods
- Lesson 01: Study PlanObtain helpful resources to accelerate your learning in the second part of the Nanodegree program.
  - Concept 01: Study Plan
  - Concept 02: Deep RL for Robotics
- Lesson 02: Deep Q-NetworksExtend value-based reinforcement learning methods to complex problems using deep neural networks.
- Lesson 03: NavigationTrain an agent to navigate a large world and collect yellow bananas, while avoiding blue bananas.Project Description - NavigationProject Rubric - Navigation
Module 02: Career Services
- Lesson 01: Opportunities in Deep Reinforcement LearningLearn about common career opportunities in deep reinforcement learning, and get tips on how to stay active in the community.
- Lesson 02: Optimize Your GitHub ProfileOther professionals are collaborating on GitHub and growing their network. Submit your profile to ensure your profile is on par with leaders in your field.Project Description - Optimize Your GitHub ProfileProject Rubric - Optimize Your GitHub Profile
Module 01: Policy-Based Methods
- Lesson 01: Study PlanObtain helpful resources to accelerate your learning in the third part of the Nanodegree program.
  - Concept 01: Study Plan
- Lesson 02: Introduction to Policy-Based MethodsPolicy-based methods try to directly optimize for the optimal policy.
- Lesson 03: Policy Gradient MethodsPolicy gradient methods search for the optimal policy through gradient ascent.
- Lesson 04: Proximal Policy OptimizationLearn what Proximal Policy Optimization (PPO) is and how it can improve policy gradients. Also learn how to implement the algorithm by training a computer to play the Atari Pong game.
- Lesson 05: Actor-Critic MethodsMiguel Morales explains how to combine value-based and policy-based methods, bringing together the best of both worlds, to solve challenging reinforcement learning problems.
- Lesson 06: Deep RL for Finance (Optional)Learn how to apply deep reinforcement learning techniques for optimal execution of portfolio transactions.
- Lesson 07: Continuous ControlTrain a double-jointed arm to reach target locations.Project Description - Continuous ControlProject Rubric - Continuous Control
Module 02: Career Services
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
Module 01: Multi-Agent Reinforcement Learning
Module 01: Special Topics in Deep Reinforcement Learning
- Lesson 01: Dynamic ProgrammingThe dynamic programming setting is a useful first step towards tackling the reinforcement learning problem.
Module 01: Neural Networks in PyTorch
Module 01: Computing Resources
- Lesson 01: Udacity WorkspacesLearn how to use Workspaces in the Udacity classroom.
  - Concept 01: Overview
  - Concept 02: Introduction to Workspaces
  - Concept 03: Workspaces: Best Practices
    Part 08 (Elective): C++ Programming
Module 01: C++ Basics
- Lesson 01: C++ Getting StartedThe differences between C++ and Python and how to write C++ code.
- Lesson 02: C++ VectorsTo program matrix algebra operations and translate your Python code, you will need to use C++ Vectors. These vectors are similar to Python lists, but the syntax can be somewhat tricky.
- Lesson 03: Practical C++Learn how to write C++ code on your own computer and compile it into a executable program without running into too many compilation errors.
- Lesson 04: C++ Object Oriented ProgrammingLearn the syntax of C++ object oriented programming as well as some of the additional OOP features provided by the language.
Module 02: Performance Programming in C++
- Lesson 01: C++ Intro to OptimizationOptimizing C++ involves understanding how a computer actually runs your programs. You’ll learn how C++ uses the CPU and RAM to execute your code and get a sense for what can slow things down.
- Lesson 02: C++ Optimization PracticeNow you understand how C++ programs execute. It’s time to learn specific optimization techniques and put them into practice. This lesson will prepare you for the lesson’s code optimization project.

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人工智能系列-2022

深度强化学习 6.0/Deep Reinforcement Learning

Deep Reinforcement Learning Nanodegree

Content

Part 01 : Introduction to Deep Reinforcement Learning

Part 02 : Value-Based Methods

Part 03 : Policy-Based Methods

Part 04 : Multi-Agent Reinforcement Learning

**Part 05 (Elective):** Special Topics in Deep Reinforcement Learning

**Part 06 (Elective):** Neural Networks in PyTorch

**Part 07 (Elective):** Computing Resources

**Part 08 (Elective):** C++ Programming

深度强化学习 6.0/Deep Reinforcement Learning

Deep Reinforcement Learning Nanodegree

Content

Part 01 : Introduction to Deep Reinforcement Learning

Part 02 : Value-Based Methods

Part 03 : Policy-Based Methods

Part 04 : Multi-Agent Reinforcement Learning

Part 05 (Elective): Special Topics in Deep Reinforcement Learning

Part 06 (Elective): Neural Networks in PyTorch

Part 07 (Elective): Computing Resources

Part 08 (Elective): C++ Programming

**Part 05 (Elective):** Special Topics in Deep Reinforcement Learning

**Part 06 (Elective):** Neural Networks in PyTorch

**Part 07 (Elective):** Computing Resources

**Part 08 (Elective):** C++ Programming