The rapid pace of research in Deep Reinforcement Learning has been driven 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 classic home console games, to modern strategy games. We propose a new benchmark called Obstacle Tower: a high visual fidelity, 3D, 3rd person, procedurally generated game environment. An agent in the 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 similar benchmarks such as the ALE, 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 initial baseline results produced by current state-of-the-art Deep RL methods as well as human players. In all cases these algorithms fail to produce agents capable of performing anywhere near human level on a set of evaluations designed to test both memorization and generalization ability. As such, we believe that the Obstacle Tower has the potential to serve as a helpful Deep RL benchmark now and into the future.

该研究提出了新的人工智能基准测试环境——障碍塔，通过该环境智能体必须学会解决低级控制和高级规划问题，同时学习像素和稀疏奖励信号。除了评估环境外，还要评估智能体在未见环境中的表现，已有的深度强化学习方法和人类玩家的基线实验结果表明，这些算法无法产生接近人类水平的智能体。

Obstacle Tower: 视觉、控制和规划的泛化挑战