Coursera Specialization is a series of courses that help you master a skill. To begin, you can enroll in the Specialization directly, or review its courses and choose the one you’d like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. You can either complete just one course or you can pause your learning or end your subscription at any time.
We have already looked at TOP 100 Coursera Specializations and today we will check out specialization to Master the Concepts of Reinforcement Learning. Implement a complete RL solution and understand how to apply AI tools to solve real-world problems.
Reinforcement Learning is a subfield of Machine Learning but is also a general-purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes action and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making.
By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science.
The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more.
Suggested Prerequisites: Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
- Formalize problems as Markov Decision Processes
- Understand basic exploration methods and the exploration/exploitation tradeoff
- Understand value functions, as a general-purpose tool for optimal decision-making
- Know how to implement dynamic programming as an efficient solution approach to an industrial control problem
This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP.
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
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
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.
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