active
library
agentum
A unified framework for sequential decision-making: from classical search to deep RL. All methods are approximations of expectimax with different representation trade-offs.
Resources & Distribution
Source Code
Package Registries
What Your RL Algorithm Actually Assumes
An interactive blog series exploring how representation choice — not algorithm choice — determines success in sequential decision-making.
The Series
- The Infinite Table — Tabular Q-learning and the cost of making zero assumptions
- The Features You Choose Are the Assumptions You Make — Linear function approximation and hand-crafted features
- The Architecture Is the Prior — Neural networks learn their own features, but the architecture decides what kind
- What You Assume vs. What You Compute — The synthesis: model-based vs. model-free, and the AIXI ideal
Each post includes interactive browser demos where you can train agents, toggle features, and watch representations diverge on the same problem.
Run Locally
python3 -m http.server 8000
# Open http://localhost:8000
No build step. No dependencies. Just static HTML, CSS, and vanilla JavaScript.
Run Tests
Open http://localhost:8000/test.html to run all self-tests in the browser.
License
MIT