  Robotic simulators are crucial for academic research and education as well as
the development of safety-critical applications. Reinforcement learning
environments -- simple simulations coupled with a problem specification in the
form of a reward function -- are also important to standardize the development
(and benchmarking) of learning algorithms. Yet, full-scale simulators typically
lack portability and parallelizability. Vice versa, many reinforcement learning
environments trade-off realism for high sample throughputs in toy-like
problems. While public data sets have greatly benefited deep learning and
computer vision, we still lack the software tools to simultaneously develop --
and fairly compare -- control theory and reinforcement learning approaches. In
this paper, we propose an open-source OpenAI Gym-like environment for multiple
quadcopters based on the Bullet physics engine. Its multi-agent and vision
based reinforcement learning interfaces, as well as the support of realistic
collisions and aerodynamic effects, make it, to the best of our knowledge, a
first of its kind. We demonstrate its use through several examples, either for
control (trajectory tracking with PID control, multi-robot flight with
downwash, etc.) or reinforcement learning (single and multi-agent stabilization
tasks), hoping to inspire future research that combines control theory and
machine learning.
