# SocNavGym: A Reinforcement Learning Gym for Social Navigation

arXiv: https://arxiv.org/abs/2304.14102 · HTML: https://mlsanity.com/paper/2304.14102/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [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)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Interactive Gibson Benchmark (iGibson 0.5): A Benchmark for Interactive Navigation in Cluttered Environments](https://mlsanity.com/paper/1910.14442/) (2019)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Relational Graph Learning for Crowd Navigation](https://mlsanity.com/paper/1909.13165/) (2019)
- [Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2011.04820/) (2020)
- [SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions](https://mlsanity.com/paper/1909.02993/) (2019)
- [SOCIALGYM: A Framework for Benchmarking Social Robot Navigation](https://mlsanity.com/paper/2109.11011/) (2021)
- [Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search](https://mlsanity.com/paper/2001.11597/) (2020)
- [Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning](https://mlsanity.com/paper/2102.13265/) (2021)
- [A Toolkit to Generate Social Navigation Datasets](https://mlsanity.com/paper/2009.05345/) (2020)
- [Generation of Human-aware Navigation Maps using Graph Neural Networks](https://mlsanity.com/paper/2011.05180/) (2020)
- [A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation](https://mlsanity.com/paper/2102.08863/) (2021)
- [DeepSocNav: Social Navigation by Imitating Human Behaviors](https://mlsanity.com/paper/2107.09170/) (2021)
- [Human-Aware Navigation Planner for Diverse Human-Robot Contexts](https://mlsanity.com/paper/2106.09971/) (2021)

## Built on by (most cited first, 3 of 3)

- [From Cognition to Precognition: A Future-Aware Framework for Social Navigation](https://mlsanity.com/paper/2409.13244/) (2024)
- [The Social Context of Human-Robot Interactions](https://mlsanity.com/paper/2508.13982/) (2025)
- [Learning to Navigate Socially Through Proactive Risk Perception](https://mlsanity.com/paper/2510.07871/) (2025)

## Abstract (arXiv)

It is essential for autonomous robots to be socially compliant while navigating in human-populated environments. Machine Learning and, especially, Deep Reinforcement Learning have recently gained considerable traction in the field of Social Navigation. This can be partially attributed to the resulting policies not being bound by human limitations in terms of code complexity or the number of variables that are handled. Unfortunately, the lack of safety guarantees and the large data requirements by DRL algorithms make learning in the real world unfeasible. To bridge this gap, simulation environments are frequently used. We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents. SocNavGym is light-weight, fast, easy-to-use, and can be effortlessly configured to generate different types of social navigation scenarios. It can also be configured to work with different hand-crafted and data-driven social reward signals and to yield a variety of evaluation metrics to benchmark agents' performance. Further, we also provide a case study where a Dueling-DQN agent is trained to learn social-navigation policies using SocNavGym. The results provides evidence that SocNavGym can be used to train an agent from scratch to navigate in simple as well as complex social scenarios. Our experiments also show that the agents trained using the data-driven reward function displays more advanced social compliance in comparison to the heuristic-based reward function.