Reinforcement Learning Specialization

Master the Concepts of Reinforcement Learning. Implement a complete RL solution and understand how to apply AI tools to solve real-world problems.

Instructors: Adam White +1 more

What you'll learn

  •   Build a Reinforcement Learning system for sequential decision making.
  •   Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more).
  •   Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution.
  •   Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning 

    • Skills you'll gain

  •   Deep Learning
  •   Machine Learning
  •   Pseudocode
  •   Sampling (Statistics)
  •   Probability Distribution
  •   Reinforcement Learning
  •   Feature Engineering
  •   Artificial Intelligence
  •   Linear Algebra
  •   Machine Learning Algorithms
  •   Artificial Intelligence and Machine Learning (AI/ML)
  •   Debugging

    • Specialization - 4 course series

    Understand how to formalize your task as a RL problem, and how to begin implementing a solution.

    By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna

    Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment

    To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution.

    Sample-based Learning Methods

    Prediction and Control with Function Approximation

    A Complete Reinforcement Learning System (Capstone)

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