Machine Learning Guide
A machine learning podcast teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models, neural networks, math, languages, frameworks, etc. Where visual ML resources provide the trees, I provide the forest. Podcasts are a great supplement during exercise, commute, chores, etc.
MLG 029 Reinforcement Learning Intro
Feb 05, 2018
Click to Play Episode
Introduction to reinforcement learning concepts
Try a walking desk to stay healthy while you study or work!
Resources
Resources best viewed here
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition
AI a Modern Approach
Sutton and Barto 2nd Ed
Deep Reinforcement Learning: CS 285 Fall 2020
RL Course by David Silver
Show Notes
- RL definition: goal, rewards, actions ** Games (Atari, Chess, Go - Lee Sedol & Alpha Go) ** AI: learning, vision / speech, action / motion, planning ** Reasoning / knowledge vs model-based Deep RL? ** Reasoning / knowledge rep (+memory?) => Differential computers (https://deepmind.com/blog/differentiable-neural-computers/) ** vs supervised. Vision = supervised. Games = action. Trading can go both ways! ** Time: Credit assignment, delayed rewards, investment
- Model-based v free ** Policy (what you do; gut reaction)
- Value-based (Q-learning) vs Policy Gradient ** PG is direct: ML -> action ** Value-based indirect: Bellman stuff -> state/action values (Q-values) -> policy
- Openai Gym, cartpole
- Frameworks ** openai/baselines ** reinforceio/tensorforce ** NervanaSystems/coach ** rll/rllab