  Learning agents can optimize standard autonomous navigation improving
flexibility, efficiency, and computational cost of the system by adopting a
wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a
fundamental modular framework to enhance navigation and learning research by
mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep
Reinforcement Learning (DRL). The paper describes the whole structure of the
PIC4rl-gym, which fully integrates DRL agent's training and testing in several
indoor and outdoor navigation scenarios and tasks. A modular approach is
adopted to easily customize the simulation by selecting new platforms, sensors,
or models. We demonstrate the potential of our novel gym by benchmarking the
resulting policies, trained for different navigation tasks, with a complete set
of metrics.
