44 datasets found

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.

The GameCLR dataset is a custom dataset created for testing the GameCLR technique for learning game state representations.

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 Atari 2600 game dataset, which consists of 4 consecutive 80x80 gray-scale game frames as the input to the network.

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

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 CARLA dataset is a complex urban-like environment with multi-agent dynamics, pedestrians, intersections, cross-traffic, roundabout, and changing weather conditions.

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.

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