Abstract : We consider the problem of multi-task reinforcement learning (MTRL) in multiple partially observable stochastic environments. This model is appropriate for the statistical analysis of electronic systems via standoff sensing. The electronic circuit is partially observable. The framework permits "life-long" learning, in which the algorithm continually improves with time, as it sees more environments and systems.