We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL)
research. In MARL, it usually requires customizing observations, rewards and
actions for each agent, changing cooperative-competitive agent-interaction, and
playing with/against a third-party agent, etc. We provide a novel modular
design, called Interface, for manipulating such routines in essentially two
ways: 1) Different interfaces can be concatenated and combined, which extends
the OpenAI Gym Wrappers concept to MARL scenarios. 2) During MARL training or
testing, interfaces can be embedded in either wrapped OpenAI Gym compatible
Environments or raw environment compatible Agents. We offer off-the-shelf
interfaces for several popular MARL platforms, including StarCraft II,
Pommerman, ViZDoom, Soccer, etc. The interfaces effectively support self-play
RL and cooperative-competitive hybrid MARL. Also, Arena can be conveniently
extended to your own favorite MARL platform.

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