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In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor critic (SAC) algorithms in a variety of challenging environments from the Open AI gym. There will be a strong focus on dealing with environments with continuous action spaces, which is of particular interest for those looking to do research into robotic control with deep reinforcement learning.
Rather than being a course that spoon feeds the student, here you are going to learn to read deep reinforcement learning research papers on your own, and implement them from scratch. You will learn a repeatable framework for quickly implementing the algorithms in advanced research papers. Mastering the content in this course will be a quantum leap in your capabilities as an artificial intelligence engineer, and will put you in a league of your own among students who are reliant on others to break down complex ideas for them.
Fear not, if it’s been a while since your last reinforcement learning course, we will begin with a briskly paced review of core topics.
The course begins with a practical review of the fundamentals of reinforcement learning, including topics such as:
Specification: Modern Reinforcement Learning: Actor-Critic Algorithms
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1 review for Modern Reinforcement Learning: Actor-Critic Algorithms
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| Price | $11.99 |
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| Provider | |
| Duration | 8 hours |
| Year | 2020 |
| Level | Expert |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
$11.99
Babak Parvizi –
Was looking for more concrete coding skills when implementing the algorithms from RL book by Sutton and Barto. This is almost verbatim so far so I’m delighted.