Omkar2118/datasets huggingface dataset:
Mario AI
Full name: Mario AI

**Mario AI** was a benchmark environment for reinforcement learning. The gameplay in Mario AI, as in the original Nintendo’s version, consists in moving the controlled character, namely Mario, through two-dimensional levels, which are viewed sideways. Mario can walk and run to the right and left, jump, and (depending on which state he is in) shoot fireballs. Gravity acts on Mario, making it necessary to jump over cliffs to get past them. Mario can be in one of three states: Small, Big (can kill enemies by jumping onto them), and Fire (can shoot fireballs).

Source: [https://github.com/zerg000000/mario-ai](https://github.com/zerg000000/mario-ai)
Image Source: [http://julian.togelius.com/Karakovskiy2012The.pdf](http://julian.togelius.com/Karakovskiy2012The.pdf)

Introducing paper: The 2009 Mario AI Competition
AtariARI
Full name: Atari Annotated RAM Interface

The **AtariARI** (**Atari Annotated RAM Interface**) is an environment for representation learning. The Atari Arcade Learning Environment (ALE) does not explicitly expose any ground truth state information. However, ALE does expose the RAM state (128 bytes per timestep) which are used by the game programmer to store important state information such as the location of sprites, the state of the clock, or the current room the agent is in. To extract these variables, the dataset creators consulted commented disassemblies (or source code) of Atari 2600 games which were made available by Engelhardt and Jentzsch and CPUWIZ. The dataset creators were able to find and verify important state variables for a total of 22 games. Once this information was acquired, combining it with the ALE interface produced a wrapper that can automatically output a state label for every example frame generated from the game. The dataset creators make this available with an easy-to-use gym wrapper, which returns this information with no change to existing code using gym interfaces.

Source: [https://arxiv.org/pdf/1906.08226.pdf](https://arxiv.org/pdf/1906.08226.pdf)
Image Source: [https://github.com/mila-iqia/atari-representation-learning](https://github.com/mila-iqia/atari-representation-learning)

Introducing paper: Unsupervised State Representation Learning in Atari
Lani

LANI is a 3D navigation environment and corpus, where an agent navigates between landmarks. **Lani** contains 27,965 crowd-sourced instructions for navigation in an open environment. Each datapoint includes an instruction, a human-annotated ground-truth demonstration trajectory, and an environment with various landmarks and lakes. The dataset train/dev/test split is 19,758/4,135/4,072. Each environment specification defines placement of 6–13 landmarks within a square grass field of size 50m×50m.

Source: [Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction](https://arxiv.org/abs/1811.04179)
Image Source: [https://arxiv.org/pdf/1809.00786.pdf](https://arxiv.org/pdf/1809.00786.pdf)

Introducing paper: Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction