Deep Reinforcement Learning for the Autonomous Adaptive Behavior of Social Robots
International Conference on Social Robotics

Social assistive robots are conceived to cooperate with humans in many
areas like healthcare, education, or assistance. In situations where the
workforce is scarce and when these machines work with special
populations like older adults or children, the behavior must be
appropriate and seem natural. In this contribution, we present a Deep
Reinforcement Learning model for the autonomous adaptive behavior of
social robots. The model emulates some aspects of human biology by
generating artificial biologically inspired functions, like sleep or
entertainment, to endow robots with long-term autonomous behavior. The
Deep Reinforcement Learning system overcomes classical Reinforcement
Learning problems such as high dimensional state-action spaces learning
which actions better suit each situation the robot is experiencing.
Besides, the system aims at maintaining the robot’s internal state in
the best possible condition.

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