Introduction to Reinforcement Learning
Explore the foundational concepts of reinforcement learning, a pivotal area in machine learning that focuses on how agents should take actions in an environment to maximize cumulative reward. This course delves into the principles and algorithms that power decision-making systems in uncertain scenarios, offering insights into policy and value function learning, exploration strategies, and real-world applications.
What You'll Learn
Core Concepts
Understand the key elements of reinforcement learning, including agents, environments, states, actions, and rewards.
Markov Decision Processes
Learn the mathematical framework that models decision-making in reinforcement learning.
Policy and Value Functions
Explore how policies and value functions are used to predict the best actions and estimate future rewards.
Exploration vs. Exploitation
Understand the balance between exploring new actions and exploiting known rewarding actions.
Reinforcement Learning Algorithms
Gain knowledge of common algorithms like Q-learning and SARSA, and understand their applications.
Practical Applications
Identify real-world scenarios where reinforcement learning can be applied effectively.
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