  The rapid pace of recent research in AI has been driven in part by the
presence of fast and challenging simulation environments. These environments
often take the form of games; with tasks ranging from simple board games, to
competitive video games. We propose a new benchmark - Obstacle Tower: a high
fidelity, 3D, 3rd person, procedurally generated environment. An agent playing
Obstacle Tower must learn to solve both low-level control and high-level
planning problems in tandem while learning from pixels and a sparse reward
signal. Unlike other benchmarks such as the Arcade Learning Environment,
evaluation of agent performance in Obstacle Tower is based on an agent's
ability to perform well on unseen instances of the environment. In this paper
we outline the environment and provide a set of baseline results produced by
current state-of-the-art Deep RL methods as well as human players. These
algorithms fail to produce agents capable of performing near human level.
