Effects of a social force model reward in robot navigation based on deep reinforcement learning
Document typeConference report
Rights accessOpen Access
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.
The final publication is available at link.springer.com
CitationGil, O.; Sanfeliu, A. Effects of a social force model reward in robot navigation based on deep reinforcement learning. A: Iberian Robotics Conference. "ROBOT 2019 : Fourth Iberian Robotics Conference, vol. 1". 2019, p. 213-224.
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.334]
- VIS - Visió Artificial i Sistemes Intel·ligents - Ponències/Comunicacions de congressos 
- Doctorat en Automàtica, Robòtica i Visió - Ponències/Comunicacions de congressos 
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