  Offline Reinforcement Learning (ORL) is a promising approach to reduce the
high sample complexity of traditional Reinforcement Learning (RL) by
eliminating the need for continuous environmental interactions. ORL exploits a
dataset of pre-collected transitions and thus expands the range of application
of RL to tasks in which the excessive environment queries increase training
time and decrease efficiency, such as in modern AAA games. This paper
introduces OfflineMania a novel environment for ORL research. It is inspired by
the iconic TrackMania series and developed using the Unity 3D game engine. The
environment simulates a single-agent racing game in which the objective is to
complete the track through optimal navigation. We provide a variety of datasets
to assess ORL performance. These datasets, created from policies of varying
ability and in different sizes, aim to offer a challenging testbed for
algorithm development and evaluation. We further establish a set of baselines
for a range of Online RL, ORL, and hybrid Offline to Online RL approaches using
our environment.
