Interpretable Reinforcement Learning Inspired by Piaget's Theory of
  Cognitive Development
  Endeavors for designing robots with human-level cognitive abilities have led
to different categories of learning machines. According to Skinner's theory,
reinforcement learning (RL) plays a key role in human intuition and cognition.
Majority of the state-of-the-art methods including deep RL algorithms are
strongly influenced by the connectionist viewpoint. Such algorithms can
significantly benefit from theories of mind and learning in other disciplines.
This paper entertains the idea that theories such as language of thought
hypothesis (LOTH), script theory, and Piaget's cognitive development theory
provide complementary approaches, which will enrich the RL field. Following
this line of thinking, a general computational building block is proposed for
Piaget's schema theory that supports the notions of productivity,
systematicity, and inferential coherence as described by Fodor in contrast with
the connectionism theory. Abstraction in the proposed method is completely upon
the system itself and is not externally constrained by any predefined
architecture. The whole process matches the Neisser's perceptual cycle model.
Performed experiments on three typical control problems followed by behavioral
analysis confirm the interpretability of the proposed method and its
competitiveness compared to the state-of-the-art algorithms. Hence, the
proposed framework can be viewed as a step towards achieving human-like
cognition in artificial intelligent systems.
