Deep Reinforcement Learning Courses and Certifications

Packt

Advanced Deep RL Algorithms and Applications

Implement advanced deep RL algorithms — DQN extensions, policy gradients, A2C/A3C — and apply them to stock trading, Atari games, TextWorld fiction, and browser navigation.

• Coursera
• 7 weeks, 1 hour a week
• Self-Paced
• Paid Course

Nicholas Renotte

Build a Street Fighter AI Model with Python - Gaming Reinforcement Learning Full Course

Builds a Street Fighter game-playing agent with Python reinforcement learning, Gym Retro, hyperparameter optimization, and Stable Baselines.

• YouTube
• 2 hours 41 minutes
• Self-Paced
• Free Video

Nicholas Renotte

Build a Doom AI Model with Python - Gaming Reinforcement Learning Full Course

Build a reinforcement learning agent that plays Doom using VizDoom, OpenAI Gym, PPO, reward shaping, and curriculum learning.

• YouTube
• 3 hours 17 minutes
• Self-Paced
• Free Video

Nicholas Renotte

Build a Mario AI Model with Python - Gaming Reinforcement Learning

Build and train a Python reinforcement learning model that learns to play Super Mario by preprocessing game frames and using the PPO algorithm.

• YouTube
• 1 hour 17 minutes
• Self-Paced
• Free Video

Nicholas Renotte

Python Reinforcement Learning Tutorial for Beginners

Train and evaluate a deep reinforcement learning agent in Python to land a spacecraft using the LunarLander environment.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Nicholas Renotte

Reinforcement Learning for Gaming - Full Python Course

Apply reinforcement learning to Mario, Doom, Street Fighter, and Chrome Dino by building, training, tuning, and testing game-playing agents in Python.

• YouTube
• 8 hours 58 minutes
• Self-Paced
• Free Video

Nicholas Renotte

Build a Chrome Dino Game AI Model with Python - AI Learns to Play Dino Game

Build a Python reinforcement learning model that learns to play the Chrome Dino game through a custom game environment and training workflow.

• YouTube
• 1 hour 42 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

MIT: Reinforcement Learning

Explores how deep neural networks learn through interaction with dynamic environments, using Q-learning and policy gradients for tasks such as gameplay.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Alexander Amini;  MIT

Reinforcement Learning

Introduces deep learning agents that learn through environmental interaction, covering Q-learning, policy gradients, continuous actions, and game-playing systems.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

Edan Meyer

RL Foundation Models Are Coming!

How AdA combines meta-learning, memory, distillation, and auto-curriculum learning to scale reinforcement learning toward foundation models.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Pascal Poupart

Distributional RL

Explore distributional reinforcement learning through return distributions, policy evaluation, Bellman equations, and C51.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Alexander Amini

Reinforcement Learning

An introduction to deep reinforcement learning through Q functions, deep Q networks, policy gradients, and game-playing systems.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Aleksa Gordić - The AI Epiphany

OpenAI - Solving Rubik's Cube with a Robot Hand - RL Paper Explained

A paper explainer on training a robotic hand in simulation to solve a Rubik’s Cube using automatic domain randomization for sim-to-real transfer.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Edan Meyer

Reinforcement Learning in RTS Games

Examines an ablation study of deep reinforcement learning agents in real-time strategy games, including action-space design, invalid-action masking, and reward shaping.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Learning to Navigate in Cities Without a Map

An introduction to training reinforcement-learning agents to navigate cities without maps using value functions, actor-critic methods, and multisite experiments.

• YouTube
• 30 minutes
• Self-Paced
• Free Video