Reinforcement Learning : Advanced Algoritms

Master advanced reinforcement learning with Python — HRL, MARL, Safe RL, Meta-Learning, and real-world projects

What you'll learn

Implement advanced reinforcement learning algorithms using Python and popular RL libraries.

Apply RL techniques to multi-agent, multi-objective, and safety-critical environments.

Design and execute real-world projects such as portfolio management and adaptive market planning.

Understand and apply meta-learning and model-based RL methods like MAML and PILCO.

Requirements

Familiarity with core reinforcement learning concepts (states, actions, rewards, policies).

Basic Python programming skills, including working with libraries like NumPy and pandas.

Understanding of common RL algorithms such as Q-learning and policy gradients.

Other RL courses by the instructor may be useful

Description

This course is designed for learners who want to go beyond the basics and master advanced reinforcement learning algorithms. Using Python, we will implement and explore a wide range of cutting-edge techniques, including Hierarchical Reinforcement Learning (HRL), Multi-Agent RL (MARL), Safe RL, Multi-Objective RL, and Meta-Learning methods such as MAML and PILCO.We’ll start with an optional Python programming refresher, covering essential syntax, data structures, and object-oriented programming — perfect if you want to brush up before diving into advanced topics.From there, you’ll work through practical coding projects using popular frameworks like Stable-Baselines3, PyQlearning, and TF-Agents. These projects include CartPole with PPO and DQN, predator–prey simulations, traveling salesman optimization with simulated annealing, portfolio management, and adaptive market planning.By the end of the course, you will:Understand and implement advanced RL algorithms from scratchApply RL to multi-agent, multi-objective, and safety-critical environmentsUse Python and major RL libraries to solve real-world problemsBuild a portfolio of projects to showcase your skillsWhether you’re a data scientist, machine learning engineer, or researcher, this course will give you the tools to push beyond standard RL and apply sophisticated decision-making systems to your work. You’ll be ready to tackle complex environments and design innovative AI solutions.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Python Programming (Optional)

Lecture 2 What is Python?

Lecture 3 Anaconda & Jupyter & Visual Studio Code

Lecture 4 Google Colab

Lecture 5 Environment Setup

Lecture 6 Python Syntax & Basic Operations

Lecture 7 Data Structures: Lists, Tuples, Sets

Lecture 8 Control Structures & Looping

Lecture 9 Functions & Basic Functional Programming

Lecture 10 Intermediate Functions

Lecture 11 Dictionaries and Advanced Data Structures

Lecture 12 Exception Handling & Robust Code

Lecture 13 Modules, Packages & Importing Libraries

Lecture 14 File Handling

Lecture 15 Basic Object-Oriented Programming (OOP)

Section 3: Hierarchical Reinforcement Learning

Lecture 16 Hierarchical Reinforcement Learning : Intro

Lecture 17 HRL Python - 1

Lecture 18 HRL Python - 2

Lecture 19 HRL Python - Output

Section 4: Imıtation Learning & Inverse Reinforcement Learning

Lecture 20 Intro

Section 5: Stable-Baselines3 Projects

Lecture 21 CartPole-v1 - Proximal Policy Optimization

Section 6: Pyqlearning Projects

Lecture 22 Simulated Annealing - Traveling Salesman Problem

Section 7: Multi-Agent Reinforcement Learning

Lecture 23 Introduction to Multi-Agent Reinforcement Learning

Lecture 24 MARL Types

Lecture 25 MARL Training

Lecture 26 MARL Challenges

Lecture 27 MARL - Predator & Prey

Lecture 28 MARL - Predator & Prey Animated Outputs

Section 8: Multi-Objective Reinforcement Learning

Lecture 29 Multi-Objective Reinforcement Learning Intro

Lecture 30 MORL Python - 1

Lecture 31 MORL Python - 2

Lecture 32 MORL Python - Output

Section 9: TF-Agents Projects

Lecture 33 What is CartPole

Lecture 34 CartPole with DQN

Section 10: Recurrent Replay Distributed DQN (R2D2)

Lecture 35 Recurrent Replay Distributed DQN (R2D2) - Intro

Lecture 36 Recurrent Replay Distributed DQN (R2D2) - Python

Section 11: C51

Lecture 37 C51 - Python

Section 12: Safe Reinforcement Learning

Lecture 38 Safe RL with Python

Section 13: MBPO

Lecture 39 MBPO Theory

Section 14: MAML - RL

Lecture 40 MAML - RL Intro

Section 15: PILCO

Lecture 41 PILCO Theory

Section 16: IMPALA

Lecture 42 IMPALA - Intro

Section 17: ME-TRPO

Lecture 43 ME-TRPO Theory

Section 18: Sequential Decision Analytics

Lecture 44 Sequential Decision Making Intro

Lecture 45 Dynamic Inventory Management - Python

Lecture 46 Adaptive Market Planning

Lecture 47 Portfolio Management

Section 19: Real-World Applications

Lecture 48 RL in Recommendation System

Lecture 49 RL in Inventory Management

Lecture 50 RL in Resource Management

This course is ideal for data scientists, machine learning engineers, AI researchers, and developers who want to go beyond standard reinforcement learning. It’s also valuable for graduate students and professionals aiming to apply advanced RL techniques to real-world problems in finance, operations, robotics, or decision-making systems.