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Learn and Liberated [Download] Modern Reinforcement Learning: Deep Q Erudition in PyTorch 2022 Udemy Course of study for Free With Direct Download Associate.
Modern Reinforcement Encyclopaedism: Deep Q Learning in PyTorch Download
How to Turn Abstruse Reinforcement Learning Research Written document Into Agents That Beat Classic Atari Games
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What you'll learn
- How to read and implement deep reinforcement learning document
- How to code Deep Q learning agents
- How to Code Double Deep Q Eruditeness Agents
- How to Cipher Dueling Inscrutable Q and Dueling Double Deep Q Learning Agents
- How to write modular and extensible deep reward learning software
- How to automate hyperparameter tuning with dictation line arguments
Requirements
- Some College Concretion
- Photo To Deep Learning
- Comfortable with Python
Verbal description
In this complete deep-water reinforcement encyclopaedism flow from you will memorise a repeatable framework for reading and implementing deep reinforcement eruditeness research document. You will read the freehanded papers that introduced the Deep Q learning, Double Abstruse Q learning, and Dueling Deep Q learning algorithms. You will so learn how to follow through these in pythonic and cryptic PyTorch code, that can constitute extended to include any upcoming deep Q encyclopedism algorithms. These algorithms volition comprise used to solve a variety of environments from the Open AI gymnasium's Atari depository library, including Pong, Breakout, and Bankheist.
You will learn the operative to making these Trench Q Acquisition algorithms work, which is how to modify the Wide-open AI Gym's Atari library to meet the specifications of the original Distant Q Learning papers. You will learn how to:
- Ingeminate actions to reduce procedure overhead
- Rescale the Atari screen images to increase efficiency
- Stack frames to give the Deep Q agent a sense of motion
- Evaluate the Deep Q agent's functioning with random no-Ops to deal with model over training
- Clip rewards to enable the Deep Q learning agent to generalize across Atari games with dissimilar score scales
If you behave not have anterior experience in reinforcement or deep reinforcement acquisition, that's nobelium job. Included in the course is a complete and concise run on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will Be taught in the linguistic context of solving the Frozen Lake surroundings from the Open AI Gym.
We will cover:
- Andre Markof conclusion processes
- Temporal difference learning
- The original Q learning algorithm
- How to solve the Bellman equating
- Value functions and natural process assess functions
- Model emancipated vs. fashion mode founded reward learnedness
- Solutions to the explore-tap dilemma, including upbeat first values and epsilon-greedy action selection
Also included is a mini course in colorful learning exploitation the PyTorch framing. This is geared for students who are acquainted the basic concepts of deep learning, but not the specifics, operating room those who are comfy with deep learning in another framework, so much atomic number 3 Tensorflow or Keras. You will read how to code a deep neural meshwork in Pytorch as well as how convolutional neural networks function. This will embody put to use in implementing a unsophisticated Artful Q scholarship agent to solve the Cartpole job from the Open AI gymnasium.
Innovative Reinforcement Learning: Deep Q Learning in PyTorch Download
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Source: https://www.udemy.com/course/deep-q-learning-from-paper-to-code/
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