Reinforcement Learning Advanced Algorithms

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.
Who this course is for
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.
Homepage

https://www.udemy.com/course/reinforcement-learning-advanced-algoritms/

Rapidgator
mrabv.Reinforcement.Learning..Advanced.Algorithms.part2.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part4.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part3.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part6.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part1.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part5.rar.html
Fikper
mrabv.Reinforcement.Learning..Advanced.Algorithms.part4.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part5.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part3.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part1.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part2.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part6.rar.html

FreeDL
mrabv.Reinforcement.Learning..Advanced.Algorithms.part5.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part4.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part3.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part6.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part2.rar.html
mrabv.Reinforcement.Learning..Advanced.Algorithms.part1.rar.html