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This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics:
Markov Decision Processes
value-iteration and policy-iteration
Q-learning fundamentals
pathfinding algorithms with Q-learning
Q-learning with neural networks
Who this course is for:
Anyone who wants to understand artificial intelligence and reinforcement learning!
Requirements
Basics AI knowledge: neural networks in the main
Markov Decision Processes
value-iteration and policy-iteration
Q-learning fundamentals
pathfinding algorithms with Q-learning
Q-learning with neural networks
Who this course is for:
Anyone who wants to understand artificial intelligence and reinforcement learning!
Requirements
Basics AI knowledge: neural networks in the main
| 1 - Introduction | 2 - Markov Decision Process MDP Theory | 3 - Markov Decision Process Value Iteration |
| 4 - Markov Decision Process Policy Iteration | 5 - Q Learning Theory | 6 - Pathfinding with QLearning |
| 7 - Exploration vs Exploitation Problem | 8 - Deep Reinforcement Learning Theory | 9 - Course Materials DOWNLOADS |
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