Reinforcement Learning

Reinforcement Learning is a type of Machine Learning where an agent learns to make decisions by taking actions in an environment to maximize a reward. Coursera's Reinforcement Learning catalogue teaches you the foundational principles and algorithms of reinforcement learning. You'll understand the exploration-exploitation tradeoff, learn about Markov Decision Processes (MDPs), and explore different methods for value function approximation. You'll also learn how to implement various reinforcement learning algorithms such as Q-Learning, Policy Gradient methods, and Deep Q-Networks (DQN). The understanding gained from these courses will equip you to handle complex real-world problems like game playing, robotics, navigation, and more.
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Explore the Reinforcement Learning Course Catalog

  • Status: Free Trial

    University of Colorado System

    Skills you'll gain: Marketing Analytics, Anomaly Detection, Advanced Analytics, Bayesian Network, Data-Driven Decision-Making, Marketing Effectiveness, Forecasting, Marketing Strategies, Time Series Analysis and Forecasting, Statistical Modeling, Simulation and Simulation Software, Predictive Analytics, Customer Retention, Reinforcement Learning

    Intermediate · Course · 1 - 4 Weeks

  • Status: New
    Status: Preview

    Northeastern University

    Skills you'll gain: PyTorch (Machine Learning Library), Applied Machine Learning, Unsupervised Learning, Reinforcement Learning, Supervised Learning, Machine Learning Algorithms, Dimensionality Reduction, Statistical Machine Learning, Machine Learning, Machine Learning Software, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Classification And Regression Tree (CART), Random Forest Algorithm, Generative Model Architectures

    Intermediate · Course · 1 - 3 Months