Curiosity Driven Deep Reinforcement Learning

🎓 Course Title: Curiosity Driven Deep Reinforcement Learning

🤖 Course Instructor: Phil Tabor

🚀 Course Headline: Master the Art of Deep Reinforcement Learning in Sparse or Reward-Free Environments!

Curriculum Overview:

Are you ready to push the boundaries of what deep reinforcement learning can achieve? In this advanced course, we delve into the fascinating world where agents learn in environments that offer no obvious rewards. 🧠✨ This is where the true potential of AI begins to unfold!

Why This Course?

• Cutting Edge Research: Learn by implementing real-world research papers from scratch.
• Hands-On Learning: Code up actor critic agents using PyTorch, without relying on GPUs.
• Parallel Processing Mastery: Discover how to efficiently use Python's multithreading capabilities for training multiple agents simultaneously.
• Advanced Techniques: Go beyond basic implementations by incorporating the latest advancements like Generalized Advantage Estimation (GAE).
• Intrinsic Motivation Explored: Implement and experiment with the Intrinsic Curiosity Module (ICM) to enhance learning in environments with sparse or no rewards at all.

Course Breakdown:

Module 1: Asynchronous Advantage Actor Critic (A3C)

• Understand the A3C algorithm and its advantages over synchronous methods.
• Implement A3C using Python multithreading for parallel processing.
• Apply Generalized Advantage Estimation to improve performance.
• Test your A3C agent in the OpenAI Gym's Atari Pong environment, achieving near-perfect scores.

Module 2: Curiosity Driven Learning

• Explore the concept of intrinsic motivation and how it can revolutionize RL.
• Implement the Intrinsic Curiosity Module to enhance learning in sparse or reward-free environments.
• Demonstrate the performance benefits of curiosity-driven exploration with hands-on examples.

Who Is This Course For? This fast-paced course is tailored for motivated and advanced students who have prior experience coding up actor critic agents. It assumes a basic understanding of reinforcement learning concepts, but we'll dive deep quickly!

What You Will Learn:

• 📝 Implement deep reinforcement learning papers from the ground up.
• Utilize multithreading in Python to train multiple agent instances concurrently.
• Code the A3C algorithm with GAE enhancements.
• Develop and test the Intrinsic Curiosity Module for environments with sparse or absent rewards.
• Modify the OpenAI Gym Atari Library to suit your experiments.
• Write modular, extensible code that can be applied to a variety of RL algorithms.

Upcoming Versions: We're starting with a PyTorch implementation, and we're excited to announce a TensorFlow 2 version in the pipeline!

Join us on this journey into the future of AI. Let's unlock the potential of deep reinforcement learning together! Enroll now and be part of this transformative experience. 🚀🧙‍♂️