Added
decisionrl.solvers: exact dynamic-programming optima (value iteration). The learned inventory policy matches the DP optimum (196.1).JointPricingInventory: coupled price and order decisions with no closed-form optimum (20 environments, 8 applied).- Gymnasium registration:
gymnasium.make("decisionrl/<Env>-v0")for every built-in environment. - Applied RL cookbook and a self-contained in-browser demo of a trained policy vs the base-stock rule.
- Nightly workflow that re-runs the multi-seed applied verification and fails on a regression.
Changed
- README rewritten in a concise, professional style.
- Benchmarks: CleanRL reference column; test coverage (86%) measured in CI with a badge.
Install: pip install -U decisionrl