Deep Reinforcement Learning & Meta-Learning Series

Photo by Tyler Nix

Deep Reinforcement Learning is about making the best decisions for what we see and what we hear. It sounds simple but making a decision is never easy. This subject is one of the hardest and one most rewarding. I try to explain things with an easy to understand angle. I don’t want to fill my readers with fancy talks that feel good but learn nothing. In reality, simplicity makes me see through the subject in better clarity. But I don’t want to skip the equations either. It just needs to be introduced in the proper manner. Understand them helps us to go deeper.

While there are still many articles need to be reviewed before publishing, the published one should give you enough details to start your journey. For the remaining articles, I will try to release them in 2019 Spring. So stay tuned.

Overview

RL — Deep Reinforcement Learning Challenges (Explore & Learn from the Past) — work in progress.

RL — Deep Reinforcement Learning Challenges (Learn how to Learn & Predict) — work in progress.

Value-learning

Q-learning

Policy Gradients

Model-based RL

RL — Guided Policy Search (GPS)

RL — Guided Policy Search (A walkthrough)

RL — Model-Based Learning with Raw Videos

Technologies

RL — Transfer Learning

RL — Prediction — work in progress

RL — PLATO Policy Learning using Adaptive Trajectory Optimization

Comparison & Tips

RL — Tips on Reinforcement Learning

RL — Comparing Reinforcement Learning (Algorithms overview)

Meta-learning

Cheat sheet

Applications

Basic

Credit and references

Reinforcement learning is a huge topic and I owe a lot of debt to many professors, researchers, and bloggers. It is impossible to quote all videos, classes, research papers, and blog that I read. In fact, there are other university courses that help me a lot but I cannot recall the institutes anymore.

For here, I want to list a few that has the biggest impacts on me.

UCL reinforcement Learning

UC Berkeley Reinforcement Learning Bootcamp

Reinforcement Learning: An introduction

Nando de Freitas class

But I want to single out the UC Berkeley Reinforcement Learning course which offers every year for now. I start watching it in 2015. It is a tough course. The lesson on LQR almost made me give up RL. But with some perseverance, that makes the biggest impact on me. I hope it can have the same impact on you too.

Deep Learning