Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper presents the Retro Learning Environment (RLE), which provides a unified interface to Atari, SNES, Sega Genesis and other consoles. Good interfaces to interesting reinforcement learning tasks are potentially very useful. One good example is the Arcade Learning Environment (ALE) of Bellemare et al. This project could go on to have a similar effect on the reinforcement learning community, but not in its current form. ALE succeeded partly because it provided a thorough set of benchmarks and suggested evaluation protocols. In order to encourage wide adoption of RLE the paper should provide guidelines for sets of interesting task, how to deal with practical issues like selection of difficulty levels, more benchmarks for interesting games, etc.
review_title: An interface to many games without established guidelines and protocols

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper presents a new environment similar to the Arcade Learning Environment, but able to support more consoles and games. It also reports performance on four SNES games, with three standard deep reinforcement learning algorithms, showing that reaching human-level performance is more challenging than on Atari games.

I believe this is a good fit for a workshop: although it has limited scientific contribution in its current form, having a new environment with the ability to play more complex games may eventually lead to significant advances in the field, so it is good to promote it. That being said, although the potential is there, it is hard to tell whether this environmnent will "take off" in the community, since the "competitors" (ALE, OpenAI Universe, DeepMind Lab...) currently enjoy higher popularity. In my opinion the only way to know is to wait and see... and giving this environment a chance to get known can't hurt!
review_title: Good workshop candidate, though limited scientific contribution

Decision: Accept