  In this paper is proposed an inclusion of the Social Force Model (SFM) into a
concrete Deep Reinforcement Learning (RL) framework for robot navigation. These
types of techniques have demonstrated to be useful to deal with different types
of environments to achieve a goal. In Deep RL, a description of the world to
describe the states and a reward adapted to the environment are crucial
elements to get the desire behaviour and achieve a high performance. For this
reason, this work adds a dense reward function based on SFM and uses the forces
in the states like an additional description. Furthermore, obstacles are added
to improve the behaviour of works that only consider moving agents. This SFM
inclusion can offer a better description of the obstacles for the navigation.
Several simulations have been done to check the effects of these modifications
in the average performance.
