15 datasets found

The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games.

The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games.

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

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...

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 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 Dynamic Frame Skip Deep Q-Network (DFDQN) dataset, which consists of 3 Atari games: Seaquest, Space Invaders, and Alien.

The dataset used in the paper is the Deep Q-Network (DQN) dataset, which consists of 15 classic Atari games.

The dataset used in the paper is the Arcade Learning Environment (ALE) dataset, which consists of 57 classic Atari games.

The dataset used in the paper is the OpenAI Gym Atari environment.

The dataset used in this paper is the Atari Learning Environment (ALE) dataset, which consists of 15 Atari video games.

The dataset used in the paper is the Arcade Learning Environment (ALE) dataset, which includes an ATARI 2600 emulator and about 50 games.

The Atari benchmark is a collection of video game environments with distinctly different dynamics, rewards, and agent embodiments.