neo4reo / 60_days_rl_challenge

Learn Deep Reinforcement Learning in depth in 60 days

I designed this Challenge for you and me: Learn Deep Reinforcement Learning in depth in 60 days!!

You heard about the amazing results achieved by Deepmind with AlphaGo Zero and by OpenAI in Dota 2! Don't you want to know how they work? This is the right opportunity for you and me to finally learn Deep RL and use it on new exciting projects.

The ultimate aim is to use these general-purpose technologies and apply them to all sorts of important real world problems. Demis Hassabis

This repository wants to guide you through the Deep Reinforcement Learning algorithms, from the most basic ones to the highly advanced AlphaGo Zero. You will find the main topics organized by week and the resources suggested to learn them. Also, every week I will provide practical examples implemented in python to help you better digest the theory. You are highly encouraged to modify and play with them!

This is my first project of this kind, so please, if you have any idea, suggestion or improvement contact me at [email protected].

During the whole challenge, I will update continuously this repository.. so stay tuned! #60DaysRLChallenge

Projects (Yet to decide)

• Q-learning
• DQN
• AC2
• ES
• AlphaGo Zero

Week 1 - Introduction

• An introduction to Reinforcement Learning by Arxiv Insights

• Introduction and course overview - CS294 by Levine

• Deep Reinforcement Learning: Pong from Pixels by Karpathy

Week 2 - RL Basics: MDP, Dynamic Programming and Model-Free Control

Week 3 - Value Function Approximation and DQN

Week 4 - A2C and A3C

Week 5 - RL in continous space - TRPO/PPO

Week 6 - Evolution Strategies and Genetic Algorithms

Week 7 - I2A

Week 8 - AlphaGoZero + Bonus

Last 4 days - Review + sharing

Best RL papers

Best resources

📺 Deep Reinforcement Learning - UC Berkeley class by Levine, check here their site.

📺 Reinforcement Learning course - by David Silver, DeepMind. Great introductory lectures by Silver, a lead researcher on AlphaGo. They follow the book Reinforcement Learning by Sutton & Barto.

📓 Reinforcement Learning: An Introduction - by Sutton & Barto. The "Bible" of reinforcement learning. Here you can find the PDF draft of the second version.

📚 Awesome Reinforcement Learning. A curated list of resources dedicated to reinforcement learning

Additional resources