ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly customizable real-time strategy (RTS) engine with three game environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a miniature version of StarCraft, captures key game dynamics and runs at 40K frameper-second (FPS) per core on a laptop. When coupled with modern reinforcement learning methods, the system can train a full-game bot against built-in AIs endto-end in one day with 6 CPUs and 1 GPU. In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like ALE [4]. Using ELF, we thoroughly explore training parameters and show that a network with Leaky ReLU [17] and Batch Normalization [11] coupled with long-horizon training and progressive curriculum beats the rule-based built-in AI more than 70% of the time in the full game of Mini-RTS. Strong performance is also achieved on the other two games. In game replays, we show our agents learn interesting strategies. ELF, along with its RL platform, is open sourced at https://github.com/facebookresearch/ELF.
The main proposal of the paper is a real-time strategy simulator specifically designed for reinforcement learning purposes. The paper presents with several details the architecture of the simulator, along with how gaming is done on it and some experimentations with the software with some RL techniques implemented in the software.
Although I think there are good values in making with software for research, I don’t think that NIPS is the right forum for presenting technical papers on them. Machine Learning Open Source Software (MLOSS) track from JMLR or relevant workshop are much relevant for that. And in the current case, a publication in the IEEE Computational Intelligence and Games (IEEE-CIG) conference might be a much better fit.
That being said, I am also quite unsure of the high relevance of real-time strategies (RTS) games for demonstrating reinforcement learning (RL). The use of RTS for benchmarking AI is not new, it has been done several time in the past. I am not convinced it will the next Atari or Go. It lacks the simplicity and elegance of Go, while not being particularly useful for practical applications. The proposed implementation is sure designed to allow fast during simulation, making learning can be done much faster (assuming that the bottleneck was the simulator, not the learning algorithms). But such a feat does not justify a NIPS paper per se, there contribution is implementation-wise, not on learning by itself.   
*** Update following rebuttal phase and comments from area chair ***
It seems that the paper still fit in the scope of the conference. I updated my evaluation accordingly, being less harsh in my scoring. However, I maintain my opinion on this paper, I still think RTS for AI is not a new proposal, and I really don't feel it will be the next Atari or Go. The work proposed appears correct in term of implementation, but in term of general usefulness, I still remain to be convinced.