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Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Le...
The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games. -
Diagnosing Bottlenecks in Deep Q-Learning Algorithms
The dataset used in the paper is a collection of expert demonstrations for various tasks, including robotic manipulation, maze navigation, and Atari games. -
Atari Environment
The dataset used in this work is the Atari environment in OpenAI Gym, created by the Arcade Learning Environment (ALE). -
OpenAI Gym and Atari games
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... -
Bootstrapped DQN
The Bootstrapped DQN dataset is a collection of 49 Atari games. -
Incentivizing Exploration in Atari
The Incentivizing Exploration in Atari dataset is a collection of 49 Atari games. -
Arcade Learning Environment
The Arcade Learning Environment (ALE) dataset is a collection of 49 Atari games. -
Dynamic Frame Skip Deep Q-Network (DFDQN) dataset
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. -
Deep Q-Network (DQN) dataset
The dataset used in the paper is the Deep Q-Network (DQN) dataset, which consists of 15 classic Atari games. -
Arcade Learning Environment (ALE) dataset
The dataset used in the paper is the Arcade Learning Environment (ALE) dataset, which consists of 57 classic Atari games. -
Atari Learning Environment
The dataset used in this paper is the Atari Learning Environment (ALE) dataset, which consists of 15 Atari video games. -
Arcade Learning Environment (ALE)
The dataset used in the paper is the Arcade Learning Environment (ALE) dataset, which includes an ATARI 2600 emulator and about 50 games.
