Advanced AI:- Deep Reinforcement Learning in Python

what you learned

  • Create different deep learning agents (including DQN and A3C)
  • Apply various advanced learning algorithms to each problem.
  • Learn cue with deep neural networks
  • Graduate Practice Policy with Neural Networks
  • Enhance learning with RBF networks
  • Use Convolutional Neural Networks with Deep Q Learning.


  • Learn the basics of Reinforcement Learning, MDPs, Dynamic Programming, Monte Carlo, Learning TD.
  • Math is useful at the college level.
  • Try building machine learning models in Python and Numpy.
  • Learn how to create ANNs and CNNs using Theano or Tensorflow.


This course is about implementing neural networks to deepen learning and strengthen learning.

If you have ever taken your first information class, you know that we can do reinforcement learning with artificial intelligence.

In particular, the combination of deep learning and reinforcement learning helped AlphaGo beat world champions in the strategy game Go, and led self-driving cars and machines that could play video games at the supernatural level.

Learning training has been around since the 1970's, but that hasn't been possible yet.

The world is changing so fast. California is changing its rules to allow self-driving car companies to inspect their cars instead of in person.

That is why reinforcement learning is a very different kind of machine learning that comes from supervised and non-supervised learning.

Supervised and non-supervised machine learning algorithms are about data analysis and prediction, while qualitative learning is about training an agent to interact with the environment and maximize its rewards.

Unlike guided and unsupervised learning algorithms, qualitative learning agents encourage - they want to achieve a goal.

That's the decent thing to do & it should end there. Why train a neural network to recognize data in a database when you can train a neural network to interact with the real world?

Although there is a lot of potential for in-depth learning and artificial intelligence, they also pose significant risks.

Bill Gates and Elon Musk have publicly outlined some of the risks that pose to AI as well as our economic stability.

As we learned in our first Reinforcement Learning course, one of the basic principles of Reinforcement Training Agents is that training AI results in unintended consequences.

AIs don't think like humans, so they come up with new and intuitive solutions to their goals, often in a way that amazes industry experts - people who are good at their job.

OpenAI is a non-profit organization founded by Elon Musk, Sam Altman (Y Combinator) and others to ensure that AI develops profitably rather than profitably.

Part of the motivation behind OpenAI is the existential threat that AI poses to humans. He believes that open cooperation is one of the keys to reducing this risk.

One of the great things about OpenAI is that they have a platform called OpenAI Gym, which we will use a lot in this course.

It allows anyone, anywhere in the world, to train trained agents in a standard environment.

In this course, we develop what we did with the more complex environments in the previous course, especially in the OpenAI gym:

Shopping cart

Mountain car

Attic Games

To train effective learning agents, we need new technology.

By looking at the TD Lambda algorithm we will increase the time difference learning, we will look at a special type of neural network called RBF network, we will look at the political gradient method and during the course we will look at DPQ. Will General Chat Chat Lounge Learning (DQN) and A3C (Asynchronous Critic Representative).

Thanks for reading, and  i will see you in class!

"If you can't do that, you don't understand."

Or, as the great physicist Richard Fanman puts it, "What I can't make, I can't understand."

My courses are where you can learn how to apply machine learning algorithms from scratch.

Other courses teach you how to link your data to the library, but do you really need help with three lines of code?

After doing the same thing with 10 datasets, you will realize that you cannot learn 10 things. I learned one thing and repeated the same 3 lines of code 10 times ...

Recommended Requirements:

Useful for college level math (math and probability)

Object Oriented Programming

Ask for notifications: if / else, loops, lists, dictations, combos

Nampi Note: Matrix and Vector Operations

Line regression

Descending gradually

Learn how to create ANNs and CNNs in Theano or TensorFlow.

Markov decision-making process (MDPs)

Learn how to program dynamically.

Here is the download links

* If your in trouble watch the video thanks! *

Torrent software for windows -> Torrent Downloader

you can join over whats app group ->  FREE COURSES 2022

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