  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.
