  We present PantheonRL, a multiagent reinforcement learning software package
for dynamic training interactions such as round-robin, adaptive, and ad-hoc
training. Our package is designed around flexible agent objects that can be
easily configured to support different training interactions, and handles fully
general multiagent environments with mixed rewards and n agents. Built on top
of StableBaselines3, our package works directly with existing powerful deep RL
algorithms. Finally, PantheonRL comes with an intuitive yet functional web user
interface for configuring experiments and launching multiple asynchronous jobs.
Our package can be found at https://github.com/Stanford-ILIAD/PantheonRL.
