Q-learning

Related lectures (29)

Page 2 of 3

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Introduces the basics of reinforcement learning, including Q-learning and epsilon-greedy policies.

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Covers the fundamentals of multilayer neural networks and deep learning.

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Covers Q-Learning, a model-free reinforcement learning algorithm, and its application to Tic-Tac-Toe with examples and quizzes.

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Explores the SARSA algorithm for reinforcement learning, focusing on updating Q-values and the importance of exploration in learning by rewards.

Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.