Table of Contents.
- Make It Happen. Reinforcement Learning as prescriptive analytics.
- Total Control. Reinforcement Learning as Optimal Control.
- The Linearization Principle. If a machine learning algorithm does crazy things when restricted to linear models, it’s going to do crazy things on complex nonlinear models too.
- The Linear Quadratic Regulator. A quick intro to LQR as why it is a great baseline for benchmarking Reinforcement Learning.
- A Game of Chance to You to Him Is One of Real Skill. Laying out the rules of the RL Game and comparing to Iterative Learning Control.
- The Policy of Truth. Policy Gradient is a Gradient Free Optimization Method.
- A Model, You Know What I Mean? Nominal control and the power of models.
- Updates on Policy Gradients. Can we fix policy gradient with algorithmic enhancements?
- Clues for Which I Search and Choose. Simple methods solve apparently complex RL benchmarks.
- The Best Things in Life Are Model Free. PID control and its connection to optimization methods popular in machine learning.
- Catching Signals That Sound in the Dark. PID for iterative learning control.
- Lost Horizons. Relating popular techniques from RL to methods from Model Predictive Control.
- Coarse-ID Control. Combining high-dimensional statistics and robust optimization for the data-driven control of uncertain systems.
Bonus Post: Benchmarking Machine Learning with Performance Profiles. The Five Percent Nation of Atari Champions.