RL + Control Theory
An authored guide bridging classical optimal control and modern reinforcement learning — in progress, with a live interactive citation graph.
rl_and_control — RL + Control Theory guide
in progressIn-progress authored guide bridging classical optimal control (Bellman, DP, MPC, LQR/LQG) with modern reinforcement learning (policy gradient, actor-critic, model-based, MPC-RL hybrids). A multi-part syllabus: RL Foundations -> Control Theory -> Convergence. Includes Python (PyTorch/Gymnasium/SB3), JAX (Brax, differentiable MPC), and Julia (ControlSystems.jl, JuMP MPC) experiment tracks. Bibliography: an indexed, status-tracked corpus across thematic sections, with method-family dossiers.
Stack: Astro · MDX · Python · PyTorch · JAX · Julia · Gymnasium · SB3 · ControlSystems.jl
What's next
Local-only; publishing surface (Astro/MDX web guide) planned, matching the ssm-foundations book-scaffold-astro pattern. Bibliography (rl_and_control/references/paper_index.md) is the data source for the /lab/research-graph/ demo (shipped 2026-05-24; last densified 2026-06-10 with pre-arXiv classics + control-text anchors).