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What you would learn in Reinforcement Learning with Pytorch course?
Artificial Intelligence is dynamically edging into our lives. It's already widely available, and we are regularly using it - often without awareness of it. Soon it will become our daily, permanent friend.
What is the best place to put Reinforcement learning in the AI world? It is one of the most exciting and fastest-growing technologies that could ultimately lead us toward General Artificial Intelligence! We can observe numerous examples of how AI can produce excellent outcomes, from hitting a superhuman level in games to solving real-world issues (robotics and healthcare).
It's without a doubt worth learning and comprehending it!
This is the reason this course was created.
We will explore a variety of subjects, with a focus on the essential and relevant specifics. Beginning with basic information and gradually increasing our knowledge and understanding, we finally get to the point where we can make an agent think humanly through video input!
What's essential is, of course, there is a need to discuss some of the basics. However, we'll concentrate on the practical aspects, and the goal is to comprehend why and how.
We will use environments from the well-known, famous OpenAI Gym to test our algorithms. We'll start with simple text games, move on to more complicated ones, and finally to more challenging Atari games.
What topics will be covered in the course?
-Introduction to Reinforcement learning
- Markov Decision Process
- Deterministic and stochastic environment
- Bellman Equation
- Q Learning
- Exploration vs. Exploitation
- Neural Networks that function as approximate approximators
- Deep Reinforcement Learning
-Improvements to DQN
- Learning through the input of video
-Reproducing some of the most well-known RL solutions
-General recommendations and tuning parameters
Reinforcement Learning basics
Deep Reinforcement Learning
Learning from the input video
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