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A regime-aware reinforcement learning workbench for synthetic trading research. It combines a hidden-regime market simulator, multiple agent baselines, a live terminal dashboard, and experiment tooling for ablations, OOD sweeps, and artifact-driven analysis.
Using multiple time series observations, split time into regimes using an array of representation learning techniques with explainability and robustness techniques for the transitions