Most reinforcement learning benchmarks reset the environment after each episode, but real enterprise systems never get a clean slate. Skyfall AI’s MORPHEUS bridges this gap by providing a persistent simulation platform for continual reinforcement learning (CRL) where past actions continuously shape future dynamics. Built on the Big World Hypothesis, MORPHEUS treats the environment as inherently non‑stationary, ensuring that any fixed policy will eventually become suboptimal.
The platform enforces three core properties: persistence, non‑stationarity, and operational complexity. Persistence means decisions compound over time, non‑stationarity guarantees that a static policy fails, and operational complexity eliminates the existence of a single optimal policy. Each environment is a self‑contained TypeScript plugin that exports Operational Descriptors, a scheduler, seed data, and documentation. Agents interact through a capability API, and each call triggers the corresponding descriptor execution.
Non‑stationarity is driven by two independent engines. A failure injection engine inserts typed disruptions—such as missing data, dependency failures, or rate limits—at configurable rates ranging from light to aggressive. An asynchronous configuration shift controller alters failure presets and demand at fixed timestamps, decoupling shifts from the training loop so agents cannot rely on update periodicity as a clock signal.
Reward is derived from three native operational verifiers: failure event signals, financial ledger status, and resource throughput. These are combined into a composite score with default weights (0.5 for failures, 0.25 each for cost and throughput). Under ideal conditions the theoretical upper bound per configuration is 0.50.
Because the action space is large, MORPHEUS uses a two‑stage initialization: a frontier model collects trajectories via ReAct, which are then used to fine‑tune a base language model via supervised fine‑tuning. All continual learning experiments start from this shared checkpoint, isolating adaptation from basic competence. Online training employs PPO as the optimizer.
Evaluation relies on six metrics: per‑configuration reward, adaptation speed, forgetting, recovery time, stability, and gap to the upper bound. Adaptation speed—measured as steps to reach half the upper‑bound reward—is the headline indicator. Baselines (PPO, HER, EWC, LCM) show varied strengths: some excel in reward, others in adaptation, but all remain far below the theoretical optimum, revealing a substantial settled‑state deficit.
For AI engineers, MORPHEUS tests regime‑shift detection without labels. For data scientists, it stresses delayed credit assignment, such as observing OTIF delivery days after dispatch. For software engineers, the TypeScript plugin format lets them swap rewards or toggle observability without altering underlying dynamics.
#AI #ReinforcementLearning #EnterpriseAI #ContinualLearning #MLOps #Productivity