Reinforcement Learning For Operations Research Problems

Harness the power of Reinforcement Learning to solve some of humanity's toughest challenges!

What you'll learn

Reinforcement Learning Fundamentals: Understand the core principles and significance of Reinforcement Learning in solving complex AI challenges.

Dynamic Programming for Resource Allocation: Develop a Policy Iteration framework to optimize resource allocation, maximizing overall performance.

Optimize Inventory Management and Route Planning: Implement Q-Learning agents to tackle inventory optimization and route planning, finding the best strategies.

Custom Environments for Deep Reinforcement Learning: Design and build customized environments to train Deep RL models for real-world route planning problem.

Deep Q-Networks (DQN) in Action: Apply DQN to solve a real-world route planning problem, experiencing the power of Deep RL in practice.

Requirements

A foundational understanding of Python programming is recommended to enhance comprehension and proficiency in the coding section.

Description

Are you ready to unlock the full potential of Artificial Intelligence? Join our exciting course on Udemy where we dive into the world of Reinforcement Learning, the driving force behind countless AI breakthroughs that simplify our lives. Now, it's time to harness this powerful technology and apply it to some of the most challenging problems humanity faces.In both industry and personal pursuits, planning and scheduling problems present formidable obstacles due to their complex nature. But fear not! Reinforcement Learning offers a solution to break through these barriers and optimize operations, driving costs down and making the world a better place to live.If you're captivated by the wonders of operations research, from resource allocation and production planning to inventory optimization and route finding, then this course is tailor-made for you. Learn to wield the impressive capabilities of Reinforcement Learning algorithms and tackle these real-world challenges with confidence.Our comprehensive course takes you on an enlightening journey through the theory of Reinforcement Learning, unraveling its connections with operations research problems. With a clear understanding of the theory, we'll delve into hands-on coding exercises, building everything from scratch using Python and essential libraries.Why settle for passive learning when you can achieve mastery through practice? You'll implement all codes from scratch, ensuring a deep comprehension of the material and enhancing your problem-solving skills.Starting with dynamic programming, we'll tackle resource allocation, and then move on to inventory optimization and route planning using Q-learning. As we progress, we'll take on the ultimate challenge: applying deep reinforcement learning in a real-world project from the ground up. Designing the environment from scratch and employing the cutting-edge PyTorch framework for Deep Learning, you'll gain the confidence to tackle any operations research problem using Reinforcement Learning.By the end of this course, you'll be equipped to apply Reinforcement Learning to any operations research problem, thanks to your solid grasp of its unique structure and its practical applications. Join us on this exciting journey and let's learn together, transforming the way we approach complex problem-solving!Are you ready to embark on this thrilling adventure? Enroll now and take the first step toward becoming a Reinforcement Learning expert!

Overview

Section 1: Overview

Lecture 1 Why This Course

Lecture 2 Who is this course for?

Lecture 3 Course objectives and Resources

Section 2: Introduction to Mathematical Optimization

Lecture 4 Supply Chain Example

Lecture 5 What is Mathematical Optimization?

Lecture 6 Different mathematical approaches and their limitations

Section 3: Introduction to Operations Research

Lecture 7 What is OR?

Lecture 8 MILP formulation example

Lecture 9 Tools for solving OR models

Section 4: Introduction to Reinforcement Learning

Lecture 10 Logic behind many algorithms

Lecture 11 Complex sequential decision making under uncertainty

Lecture 12 Dynamic programming

Lecture 13 Dynamic programming example

Lecture 14 Markov Decision Process

Lecture 15 Bellman equation

Lecture 16 MDP components

Lecture 17 Reinforcement Learning components

Lecture 18 Monte Carlo Learning, Temporal Difference Learning

Lecture 19 Q-Learning

Lecture 20 Off Policy, On Policy Learning

Lecture 21 Deep Reinforcement Learning Foundations

Section 5: Resource Allocation with Dynamic Programming

Lecture 22 Introduction to Google Colab

Lecture 23 Resource Allocation problem description

Lecture 24 Define problem parameters

Lecture 25 Define algorithm parameters

Lecture 26 Transition matrix-Part 1

Lecture 27 Transition matrix-Part 2

Lecture 28 General Policy Iteration (GPI) explained

Lecture 29 Policy Evaluation

Lecture 30 Policy Improvement

Lecture 31 Interpret results

Section 6: Inventory Optimization with Q-Learning

Lecture 32 Inventory management optimization problem description

Lecture 33 Define parameters

Lecture 34 Action Policy

Lecture 35 Reward signal

Lecture 36 Bellman equation

Lecture 37 Optimal policy

Section 7: Travel Sales Person with Q-Learning

Lecture 38 TSP problem description

Lecture 39 Define parameters

Lecture 40 Action selection strategy

Lecture 41 Update Q-table

Lecture 42 Q-Learning algorithm

Lecture 43 Extract best route

Section 8: Travel Sales Person with Deep Q-Networks

Lecture 44 How to design customized environment based on OpenAI gym library

Lecture 45 Define parameters

Lecture 46 reset() function

Lecture 47 step() function

Lecture 48 Test the environment

Lecture 49 DQN network

Lecture 50 Reply buffer idea

Lecture 51 Reply buffer class

Lecture 52 Initialization of DQN agent

Lecture 53 Action selection strategy

Lecture 54 Why policy and target network?

Lecture 55 Get Q-values

Lecture 56 Update policy network

Lecture 57 Update target network

Lecture 58 Visualize loss values

Lecture 59 Main training loop

Lecture 60 Get the best route

Applied Data Scientists,Machine Learning Developers,Operations Research Specialists,Data Analysts,Planning and Scheduling Specialists