17 datasets found

The dataset used for training the SAI agent, containing 7x7 Go games with multiple komi values.

The MineRL-v0 dataset contains human demonstration data for tasks in Minecraft.

The MineRL BASALT competition dataset contains human demonstration data for four tasks in Minecraft.

The dataset used in this work is the Breakout, SeaQuest, Space Invaders, and Qbert environments.

The Arcade Learning Environment (ALE) is a lasting and indispensable element of the RL researcher’s toolbox. It is also the focus of our work. Since its inception, hundreds of...

DMLab-30 is a benchmark for multitask reinforcement learning in partially observable environments.

The dataset used in this paper is the Car Racing game dataset, which consists of pixel frames of a car racing game.

The dataset used in this paper is the OpenAI Gym Environment dataset, which consists of various games and environments.

The dataset used in this paper is the Atari 2600 games dataset, which consists of 50 Atari 2600 games.

The dataset used in the paper is a procedurally generated environment called ProcGen.

The MineRL dataset is a large-scale dataset of Minecraft demonstrations, focusing on the development of sample-efficient reinforcement learning algorithms for mining items in...

The Atari 2600 domain dataset, used for training and testing reinforcement learning algorithms.

The Atari dataset is a collection of 55 Atari games, each with its own set of rules and objectives.

The Atari 57 benchmark is a collection of 57 Atari games, each with its own set of states and actions.

The dataset used in the Generative Adversarial Exploration for Reinforcement Learning paper.

The dataset used in the paper is the Atari 2600 dataset, which consists of 49 games. The dataset is used to test the Successor Uncertainties algorithm.

The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used Atari environments and board games to evaluate the proposed algorithm.