The Arcade Learning Environment (ALE) is an object-oriented framework that allows researchers to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design.
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The DQN Replay Dataset was collected as follows: We first train a DQN agent, on all 60 Atari 2600 games with sticky actions enabled for 200 million frames (standard protocol) and save all of the experience tuples of (observation, action, reward, next observation) (approximately 50 million) encountered during training.
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Atari-HEAD is a dataset of human actions and eye movements recorded while playing Atari videos games. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded. The gaze data was recorded using an EyeLink 1000 eye tracker at 1000Hz. The human subjects are amateur players who are familiar with the games. The human subjects were only allowed to play for 15 minutes and were required to rest for at least 15 minutes before the next trial. Data was collected from 4 subjects, 16 games, 175 15-minute trials, and a total of 2.97 million frames/demonstrations.
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The Atari Grand Challenge dataset is a large dataset of human Atari 2600 replays. It consists of replays for 5 different games: * Space Invaders (445 episodes, 2M frames) * Q*bert (659 episodes, 1.6M frames) * Ms.Pacman (384 episodes, 1.7M frames) * Video Pinball (211 episodes, 1.5M frames) * Montezuma’s revenge (668 episodes, 2.7M frames)
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The AtariARI (Atari Annotated RAM Interface) is an environment for representation learning. The Atari Arcade Learning Environment (ALE) does not explicitly expose any ground truth state information. However, ALE does expose the RAM state (128 bytes per timestep) which are used by the game programmer to store important state information such as the location of sprites, the state of the clock, or the current room the agent is in. To extract these variables, the dataset creators consulted commented disassemblies (or source code) of Atari 2600 games which were made available by Engelhardt and Jentzsch and CPUWIZ. The dataset creators were able to find and verify important state variables for a total of 22 games. Once this information was acquired, combining it with the ALE interface produced a wrapper that can automatically output a state label for every example frame generated from the game. The dataset creators make this available with an easy-to-use gym wrapper, which returns this infor
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RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, we provide the datasets with a unified API which makes it easy for the practitioner to work with all data in the suite once a general pipeline has been established. This is a dataset accompanying the paper RL Unplugged: Benchmarks for Offline Reinforcement Learning.
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