Multi-armed bandit

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Related lectures (9)

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Reinforcement Learning: Q-Learning

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

Visual Intelligence and Learning: Insights and Applications

Explores visual intelligence, robotics perception, and multi-task learning techniques in computer vision.

Bullet Arm: Robotic Manipulation Benchmark

Introduces BulletArm, an open-source robotic manipulation benchmark and learning framework, covering design goals, benchmark tasks, and learning algorithms.

Chemical Reaction Optimization: Multi-Task Learning

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Learning Models for Belief-Driven Mobile Manipulation Tasks

Delves into learning models for belief-driven mobile manipulation tasks in open environments, covering actions like leaping, grasping, and stacking.

Reinforcement Learning: Bandit Problems

Covers the convergence in expectation for the Q value in reinforcement learning.

Reinforcement Learning: BackUp Diagrams

Introduces the BackUp diagram as a key graphic representation in reinforcement learning.

Reinforcement Learning: One-step Horizon (Bandit Problems)

Covers Bandit Problems in Reinforcement Learning, focusing on one-step horizon games and Q-values.

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