Playing SNES in the Retro Learning Environment
By Nadav Bhonker, Shai Rozenberg, Itay Hubara. 2016
19 citations recorded by the source corpus.
Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. In recent years, extensive research was carried out in the field of reinforcement learning and numerous algorithms were introduced, aiming to learn how to perform human tasks such as playing video games. As a result, the Arcade Learning Environment (ALE) (Bellemare et al., 2013) has become a commonly used benchmark environment allowing algorithms to train on various Atari 2600 games. In many games the state-of-the-art algorithms outperform humans. In this paper we introduce a new learning environment, the Retro Learning Environment --- RLE, that can run games on the Super Nintendo Entertainment System (SNES), Sega Genesis and several other gaming consoles. The environment is expandable, allowing for more video games and consoles to be easily added to the environment, while maintaining the same interface as ALE. Moreover, RLE is compatible with Python and Torch. SNES games pose a significant challenge to current algorithms due to their higher level of complexity and versatility.
Subfields: Artificial Intelligence, Developmental and Educational Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Yoshua Bengio
- Chelsea Finn
- Timothy P. Lillicrap
- Ruslan Salakhutdinov
- Quoc V. Le
- Yuxi Li
In Developmental and Educational Psychology
- Timothy P. Lillicrap
- David Silver
- Oriol Vinyals
- Sergey Bartunov
- John Schulman
- Hado van Hasselt
- Pieter Abbeel
- J. Urgen Schmidhuber
Citations in the corpus
- StarCraft II: A New Challenge for Reinforcement Learning (2017, methodology): “The ALE is a prominent example in a rich tradition of video game benchmarks for AI [30], including Super Mario [35], Ms Pac-Man [26], Doom [13], Unreal Tournament [11], as well as general video game-playing frameworks [29, 5] and competitions [23].”
- Gotta Learn Fast: A New Benchmark for Generalization in RL (2018, methodology): “Like RLE [7], gym-retro uses the libretro API 1 to interface with game emulators, making it very easy to add new emulators to gym-retro.”
- Using Unity to Help Solve Intelligence (2020, background): “, 2013), the Retro Learning Environment (Bhonker et al., 2016; Nichol et al., 2018), multiplayer first-person game Quake III (Jaderberg et al.”