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Reinforcement Learning Projects with Python
Rating: 4.5 out of 5(8 ratings)
62 students

Reinforcement Learning Projects with Python

Hands-on reinforcement learning with Python through real projects
Created byAdvancedor Academy
Last updated 11/2025
English
English [Auto],

What you'll learn

  • Implement reinforcement learning projects in Python from start to finish.
  • Apply reinforcement learning to games, optimization, and real-world tasks
  • Build projects for recommendation systems, inventory, and resource management.
  • Use reinforcement learning methods like DQN, SAC, and simulated annealing.

Course content

13 sections • 28 lectures • 7h 53m total length

  • Introduction2:55

    Develop practical reinforcement learning projects in Python, from blackjack to route optimization and inventory management, showing how agents interact with environments, rewards, and policies improve with experience.

  • Before The Course5:15

    This course teaches reinforcement learning by showing working code on screen while students type it themselves, emphasizing understanding over file downloads and learning through practice and mistakes.

  • About This Course2:14

    Learn how reinforcement learning applies to manufacturing challenges, from production line balancing and preventative maintenance to tuning machining and injection molding parameters using Python simulations and DQN.

  • Ratings2:54

    Discusses how Udemy ratings may be unfair for long courses, urges rating only after experiencing at least half the content, and explains pacing designed for an international audience.

  • Key Terms 111:09

    Learn the foundations of sequential decision making through states, actions, rewards, deterministic and stochastic policies, and Markov decision processes with gamma discounting and value functions for optimal strategies.

  • Key Terms 210:16

    Connect the Bellman equation to dynamic programming by decomposing a state's value into the immediate reward and the discounted future value, and explore reinforcement learning with Q-learning and policy gradients.

  • Key Terms 38:45

    Compare model-based and model-free reinforcement learning, detailing transition and reward models, state and action spaces, and the Markov decision process framework with the Bellman objective.

  • Dynamic Programming15:09

    Explore dynamic programming, using memoization to reuse results and convert exponential problems into polynomial ones, with space time trade off, and top-down and bottom-up approaches, optimal substructure, and overlapping subproblems.

  • Bellman Equations - Theory12:28

    Explore the Bellman equation, the backbone of dynamic programming, showing how state values relate to future rewards and enabling optimal substructure, value decomposition, and model-free Q-learning.

  • Policy Approximations22:45

    Master policy approximations for scalable reinforcement learning in high-dimensional environments, using value-based, policy gradient, and actor-critic methods with PPO, SAC, and TRPO.

  • Policy Classes45:39

    Explore the universal five-element framework for sequential decisions and compare policy classes (PFA, CFA, VFA, DLA) with hybrids, illustrating when to use lookahead and reinforcement learning approaches.

Requirements

  • Basic knowledge of Python programming (variables, loops, functions).
  • Familiarity with libraries such as NumPy and Matplotlib is helpful.
  • Some exposure to machine learning concepts will make the course easier to follow, but it’s not strictly required.

Description

Reinforcement learning is one of the most exciting areas in machine learning, and the best way to learn it is by working on projects. This course is designed to give you practical, step-by-step experience with reinforcement learning using Python. Instead of only focusing on theory, we’ll build working solutions that you can run, test, and extend.

We begin with a classic example, the Blackjack game, to introduce the core ideas of agents, rewards, and policies. From there, the course moves into optimization and decision-making problems. You’ll explore the Traveling Salesman Problem with simulated annealing, and then see how reinforcement learning can be applied in real applications such as recommendation systems, inventory management, and resource allocation.

The course also covers more advanced use cases. We’ll look at CNC machining parameter optimization with Soft Actor-Critic (SAC), chemical batch process optimization with Deep Q-Networks (DQN), and network optimization in two parts. In the final section, we’ll introduce safe reinforcement learning, which has become increasingly important in real-world systems where safety constraints must be respected.

By the end of this course, you will have implemented a range of reinforcement learning projects in Python and gained a deeper understanding of how these methods can be applied across different domains. The course is suitable for learners who already have some Python experience and want to strengthen their knowledge of machine learning through practice.

If you’re looking for a hands-on way to learn reinforcement learning and see it applied in diverse problems, this course will guide you through each step.

Who this course is for:

  • Python developers who want to apply reinforcement learning in real projects.
  • Students and researchers in machine learning, AI, or optimization.
  • Data scientists and engineers looking to expand their skills into reinforcement learning.

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