An Enactive approach to autonomous agent and robot learning

A novel way to model an agent interacting with an environment is introduced, called an Enactive Markov Decision Process (EMDP). An EMDP keeps perception and action embedded within sensorimotor schemes rather than dissociated. Instead of seeking a goal associated with a reward, as in reinforcement learning, an EMDP agent is driven by two forms of self-motivation: successfully enacting sequences of interactions (autotelic motivation), and preferably enacting interactions that have predefined positive values (interactional motivation). An EMDP learning algorithm is presented. Results show that the agent develops a rudimentary form of self-programming, along with active perception as it learns to master the sensorimotor contingencies afforded by its coupling with the environment.