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.

翻译：自主机器人在人类密集环境中导航时，必须遵循社交规范。机器学习，尤其是深度强化学习，近年来在社交导航领域获得了显著关注。这在一定程度上归因于其生成的策略不受代码复杂度或处理变量数量等人为限制的约束。然而，深度强化学习算法缺乏安全保障且需要大量数据，使得在现实世界中学习不可行。为弥合这一差距，仿真环境被广泛采用。我们提出了SocNavGym——一种先进的社交导航仿真环境，能够生成多样化的社交导航场景，并促进智能社交体的开发。SocNavGym轻量级、快速、易用，且可轻松配置以生成不同类型的社交导航场景。它还可配置为适配不同的人工设计或数据驱动的社交奖励信号，并生成多种评估指标以基准测试智能体的性能。此外，我们还提供了一项案例研究：利用SocNavGym训练Dueling-DQN智能体学习社交导航策略。结果表明，SocNavGym可从头训练智能体在简单和复杂社交场景中导航。我们的实验同样显示，与基于启发式的奖励函数相比，采用数据驱动奖励函数训练的智能体表现出更优的社交合规性。