# Reward Shaping with Subgoals for Social Navigation

arXiv: https://arxiv.org/abs/2104.06410 · HTML: https://mlsanity.com/paper/2104.06410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization](https://mlsanity.com/paper/2002.11635/) (2020)
- [Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards](https://mlsanity.com/paper/1910.04450/) (2019)
- [Learning from Trajectories via Subgoal Discovery](https://mlsanity.com/paper/1911.07224/) (2019)
- [Efficient and Trustworthy Social Navigation Via Explicit and Implicit Robot-Human Communication](https://mlsanity.com/paper/1810.11556/) (2018)
- [Social Navigation with Human Empowerment driven Deep Reinforcement Learning](https://mlsanity.com/paper/2003.08158/) (2020)

## Abstract (arXiv)

Social navigation has been gaining attentions with the growth in machine intelligence. Since reinforcement learning can select an action in the prediction phase at a low computational cost, it has been formulated in a social navigation tasks. However, reinforcement learning takes an enormous number of iterations until acquiring a behavior policy in the learning phase. This negatively affects the learning of robot behaviors in the real world. In particular, social navigation includes humans who are unpredictable moving obstacles in an environment. We proposed a reward shaping method with subgoals to accelerate learning. The main part is an aggregation method that use subgoals to shape a reinforcement learning algorithm. We performed a learning experiment with a social navigation task in which a robot avoided collisions and then reached its goal. The experimental results show that our method improved the learning efficiency from a base algorithm in the task.