Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game
  Environment for Multi-agent Reinforcement Learning
  The development of deep reinforcement learning (DRL) has benefited from the
emergency of a variety type of game environments where new challenging problems
are proposed and new algorithms can be tested safely and quickly, such as Board
games, RTS, FPS, and MOBA games. However, many existing environments lack
complexity and flexibility and assume the actions are synchronously executed in
multi-agent settings, which become less valuable. We introduce the Fever
Basketball game, a novel reinforcement learning environment where agents are
trained to play basketball game. It is a complex and challenging environment
that supports multiple characters, multiple positions, and both the
single-agent and multi-agent player control modes. In addition, to better
simulate real-world basketball games, the execution time of actions differs
among players, which makes Fever Basketball a novel asynchronized environment.
We evaluate commonly used multi-agent algorithms of both independent learners
and joint-action learners in three game scenarios with varying difficulties,
and heuristically propose two baseline methods to diminish the extra
non-stationarity brought by asynchronism in Fever Basketball Benchmarks.
Besides, we propose an integrated curricula training (ICT) framework to better
handle Fever Basketball problems, which includes several game-rule based
cascading curricula learners and a coordination curricula switcher focusing on
enhancing coordination within the team. The results show that the game remains
challenging and can be used as a benchmark environment for studies like
long-time horizon, sparse rewards, credit assignment, and non-stationarity,
etc. in multi-agent settings.
