29 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 dataset used in this work is the Atari environment in OpenAI Gym, created by the Arcade Learning Environment (ALE).

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...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors conducted experiments on several representative tasks from the OpenAI Gym and...

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 TorchCraft dataset is a collection of games played by a reinforcement learning agent, which can be used to train and evaluate reinforcement learning algorithms.

The Bootstrapped DQN dataset is a collection of 49 Atari games.

The Incentivizing Exploration in Atari dataset is a collection of 49 Atari games.

The Arcade Learning Environment (ALE) dataset is a collection of 49 Atari games.

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 dataset used in the paper is the Rainbow dataset, which is a combination of six extensions to the DQN algorithm.