Reinforcement Learning Masterclass

Master Reinforcement Learning: From Basics to Advanced Applications

What you'll learn

Understand the key concepts and components of reinforcement learning, including MDPs, policies, rewards, and value functions

Apply algorithms like SARSA, Q-Learning, REINFORCE, PPO, TRPO, SAC, and DQN in Python

Use modern libraries like Stable-Baselines3 and TF-Agents to solve real-world problems with RL

Implement actor-critic and policy gradient methods using neural networks

nderstand how to apply reinforcement learning in multi-agent and multi-objective environments

Build end-to-end projects such as inventory management, recommendation systems, and resource allocation with RL

Requirements

Basic understanding of Python and Numpy is recommended. Familiarity with probability, linear algebra, or machine learning will help, but not mandatory — the course starts from the foundations and builds up gradually.

Description

Welcome to the Reinforcement Learning Course! This course is designed to take you from the basics of Reinforcement Learning (RL) to advanced techniques and applications. Whether you're a data scientist, researcher, software developer, or simply curious about AI, this course will provide you with valuable insights and hands-on experience in the field of RL.In this course, you will:Understand the fundamentals of Reinforcement Learning: Learn about the core components of RL, including agents, environments, actions, rewards, and states.Explore Markov Decision Processes (MDPs): Study the concepts of policies, value functions, and solving MDPs using dynamic programming.Solve Multi-Armed Bandit Problems: Understand ε-greedy actions, Thompson sampling, and the exploration-exploitation trade-off.Master Temporal-Difference Learning: Learn about TD learning, SARSA, and Q-Learning.Learn Deep Q-Learning: Discover Deep Q-Networks (DQN), experience replay, and target networks.Apply Policy Gradient Methods: Explore algorithms like REINFORCE, Advantage Actor-Critic (A2C), and Asynchronous Advantage Actor-Critic (A3C).Implement Advanced Techniques: Learn about Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), and more.Understand Evolution Strategies and Genetic Algorithms: Get an introduction to these powerful optimization techniques.Explore Model-Based RL: Learn about dynamic programming and the Dyna-Q algorithm.Investigate Hierarchical RL: Study hierarchical policies, the options framework, and MAXQ value function decomposition.Examine Curiosity-Driven Exploration: Understand intrinsic motivation in RL and curiosity-driven agents.Learn Bayesian Methods in RL: Study Bayesian optimization with Gaussian processes and Thompson sampling.Discover Distributed RL: Explore scalable RL architectures and distributed experience replay.Understand Meta-Reinforcement Learning: Learn about learning to learn and gradient-based meta-RL.Explore Multi-Agent RL: Study multi-agent systems, cooperative vs. competitive scenarios, and advanced algorithms like MADDPG and MAPPO.Focus on Safe RL: Learn about safety constraints, constrained policy optimization, and risk-aware RL.Study Inverse RL: Understand the basics, applications, and reward shaping in inverse RL.Perform Off-Policy Evaluation: Learn about importance sampling, doubly robust estimators, and other methods.Use Function Approximation in RL: Discover linear function approximation and the role of neural networks in RL.Optimize with Sequential Model-Based Techniques: Learn about Bayesian optimization and Gaussian processes in RL.Balance Multiple Objectives in RL: Study multi-objective RL and Pareto optimality.Understand Deep Recurrent Q-Networks (DRQN): Learn about memory-augmented neural networks and applications in partially observable environments.Explore Implicit Quantile Networks (IQN): Study distributional RL and quantile regression.Investigate Neural Episodic Control (NEC): Understand episodic memory in RL and the NEC algorithm.Implement Policy Iteration with Function Approximation: Learn about iterative policy evaluation and generalized policy iteration.Apply RL in Various Fields: Study applications of RL in robotics, autonomous systems, finance, supply chain management, and marketing.By the end of this course, you will have a thorough understanding of Reinforcement Learning and be equipped to apply it to solve complex problems in various domains. Join us and become proficient in this cutting-edge field!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 How You Should Study This Course?

Lecture 3 Curriculum

Lecture 4 What's Reinforcement Learning?

Lecture 5 Components of Reinforcement Learning

Section 2: Mathematical Foundations

Lecture 6 Probability Theory Essentials

Lecture 7 Markov Decision Processes

Lecture 8 Markov Decision Processes - Case

Lecture 9 Markov Decision Processes - Python

Lecture 10 Markov Decision Processes Code Output

Lecture 11 Dynamic Programming Principles

Lecture 12 Dynamic Programming - Case

Lecture 13 Dynamic Programming - Mathematical Model

Lecture 14 Dynamic Programming - Python Code

Lecture 15 Dynamic Programming - Output

Lecture 16 Probability Distributions - Theory

Section 3: Dynamic Programming

Lecture 17 Policy Evaluation

Lecture 18 Iterative Policy Evaluation Algorithm with Python

Section 4: Monte Carlo Methods

Lecture 19 Blackjack - Intro

Lecture 20 Blackjack Python

Lecture 21 Blackjack Output

Section 5: Temporal Difference Learning

Lecture 22 What is SARSA?

Lecture 23 SARSA - Taxi Implementation

Lecture 24 SARSA - Taxi & Visual

Lecture 25 Q-Learning Intro

Lecture 26 Frozen Lake

Lecture 27 Frozen Lake Python

Lecture 28 Cliff Walking Python

Section 6: Function Approximation

Lecture 29 Function Approximation in RL

Lecture 30 Neural Networks in Reinforcement Learning

Section 7: Policy Gradient Methods

Lecture 31 What is Reinforce?

Lecture 32 REINFORCE - Python

Lecture 33 Generalized Advantage Estimation (GAE)

Lecture 34 Generalized Advantage Estimation (GAE) - Python

Lecture 35 Advantage Actor-Critic (A2C)

Lecture 36 Asynchronous Advantage Actor-Critic (A3C)

Lecture 37 Deterministic Policy Gradient (DPG)

Lecture 38 DDPG (Deep Deterministic Policy Gradient)

Lecture 39 TD3 (Twin Delayed DDPG)

Lecture 40 SAC (Soft Actor-Critic)

Lecture 41 TRPO Intro

Lecture 42 Trust Region Policy Optimization (TRPO) - Python 1

Lecture 43 Trust Region Policy Optimization (TRPO) - Python 2

Lecture 44 Trust Region Policy Optimization (TRPO) - Python 3

Lecture 45 Trust Region Policy Optimization (TRPO) - Python 4

Lecture 46 TRPO - Output

Lecture 47 Proximal Policy Optimization

Lecture 48 ME-TRPO

Section 8: Deep Q-Networks

Lecture 49 DQN Intro

Section 9: Hierarchical Reinforcement Learning

Lecture 50 Hierarchical Reinforcement Learning : Intro

Lecture 51 HRL Python - 1

Lecture 52 HRL Python - 2

Lecture 53 HRL Python - Output

Section 10: Imıtation Learning & Inverse Reinforcement Learning

Lecture 54 Intro

Section 11: Stable-Baselines3 Projects

Lecture 55 CartPole-v1 - Proximal Policy Optimization

Section 12: Pyqlearning Projects

Lecture 56 Simulated Annealing - Traveling Salesman Problem

Section 13: Multi-Agent Reinforcement Learning

Lecture 57 Introduction to Multi-Agent Reinforcement Learning

Lecture 58 MARL Types

Lecture 59 MARL Training

Lecture 60 MARL Challenges

Lecture 61 MARL - Predator & Prey

Lecture 62 MARL - Predator & Prey Animated Outputs

Section 14: Multi-Objective Reinforcement Learning

Lecture 63 MORL Intro

Lecture 64 MORL Python - 1

Lecture 65 MORL Python - 2

Lecture 66 MORL Python - Output

Section 15: TF-Agents Projects

Lecture 67 What is CartPole

Lecture 68 CartPole with DQN

Section 16: Safe Reinforcement Learning

Lecture 69 Safe RL with Python

Section 17: Sequential Decision Analytics

Lecture 70 Sequential Decision Making Intro

Lecture 71 SDA Project with Julia - 1

Lecture 72 Dynamic Inventory Management - Python

Lecture 73 Adaptive Market Planning

Lecture 74 Portfolio Management

Lecture 75 Airline Pricing with Python - Code

Lecture 76 Airline Pricing - Output

Lecture 77 SDA Project with Julia - 2

Section 18: Advanced Topics in Reinforcement Learning

Lecture 78 Recurrent Replay Distributed DQN (R2D2) with Python

Lecture 79 C51

Section 19: Real-World Applications

Lecture 80 RL in Resource Management

Lecture 81 RL in Network Optimization - Part 1

Lecture 82 RL in Network Optimization - Part 2

Lecture 83 RL in Recommendation System

Lecture 84 RL in Inventory Management

Section 20: Goodbye!

Lecture 85 Closure

This course is for anyone who wants to learn reinforcement learning from scratch and apply it to real-world problems — whether you're a data scientist, engineer, researcher, or an advanced student aiming to master RL from both theoretical and practical angles.