classically here.

Partially observability and multi-agent approaches have been considered widely in AI [@KaelblingLittman; @book:Russel; @book:Sigaud] and reinforcement learning [@Fujita; @Doshi]. In this paper, we introduce the notion of a partially observable environment to the PS learning method using a set of belief states in the formulation of efficiency. As our examples, we use a multi-agent invasion setting where another agent as an interpreter can teach the percepts to the defender. Afterwards, every agent can be considered as a simultaneous learner-teacher where there could be some game theoretical aspects therein.

A one way partially observable projective simulation (POPS)
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In a partially observable environment, the agent may be unable to observe the current state. In this approach, a fully observable environment is just a special case of the partial observability when the belief state ($b$) is equal to the current percept ($s$) for every real percept $s$ at all times. It is believed that partially observable AI can add more realistic examples to the entirely observable AI scenarios.

We track a multi-agent partially observable example in this article, though our following formulation is independent of any multi-agent hypothesis and I think it will be beneficial as an interior network structure 