Dive into the world of Reinforcement Learning through this comprehensive 3.5-hour tutorial series. Begin with an introduction to Q Learning and its table-based approach, then progress to understanding the Q Learning algorithm and agent implementation. Analyze the Q-Learning agent's performance before learning how to create a custom Reinforcement Learning (RL) environment. Advance to Deep Q Learning with DQN, and finally, master the techniques for training and testing a Deep reinforcement learning (DQN) agent. Gain practical knowledge and hands-on experience in this cutting-edge field of artificial intelligence.
Overview
Syllabus
Q Learning Intro/Table - Reinforcement Learning p.1.
Q Learning Algorithm and Agent - Reinforcement Learning p.2.
Q-Learning Agent Analysis - Reinforcement Learning p.3.
Creating A Reinforcement Learning (RL) Environment - Reinforcement Learning p.4.
Deep Q Learning w/ DQN - Reinforcement Learning p.5.
Training & Testing Deep reinforcement learning (DQN) Agent - Reinforcement Learning p.6.
Taught by
sentdex
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