Super Mario Bros Nes -

Research involving Super Mario Bros. on the NES often focuses on training agents to navigate complex environments using only visual input. Key papers and projects include:

While there isn't a single famous academic "Deep Paper" by that exact title, the phrase typically refers to research in using Super Mario Bros. (NES) as a primary benchmark for AI agents . Core Research Themes

: It is credited with reviving the video game industry after the 1983 crash. Super Mario Bros NES

: Many implementations, such as those found on Paperspace , detail building Double Deep Q-Networks to teach agents how to clear Level 1-1 by updating "Q-tables" based on reward functions.

: This research paper discusses using Deep RL to tackle the vast state spaces of NES titles, noting that in an average Mario level, a character can occupy thousands of different x-positions across multiple timesteps. Research involving Super Mario Bros

In contrast to modern AI complexity, the original 1985 game was a feat of extreme optimization: : The entire game is only 32 KB .

The "depth" of the NES original is also frequently discussed in the context of its legacy: (NES) as a primary benchmark for AI agents

: It uses 32KB for game code and music (PRG ROM) and 8KB for graphics (CHR ROM).