ALE

E438355

ALE is a widely used research platform that provides a common interface to hundreds of Atari 2600 games for developing and evaluating artificial intelligence and reinforcement learning algorithms.

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ALE canonical 1

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Statements (49)

Predicate Object
instanceOf reinforcement learning benchmark suite
research platform
software framework
acronymFor Arcade Learning Environment NERFINISHED
benchmarkProperty fixed action spaces per game
high-dimensional visual input
long-term credit assignment challenges
shared observation format across games
sparse and delayed rewards in many games
domain artificial intelligence research
reinforcement learning research
enables comparison of RL algorithms on common tasks
environmentType discrete-time Markov decision process
evaluationMetric average return over episodes
game score
fullName Arcade Learning Environment NERFINISHED
gamePlatform Atari 2600 NERFINISHED
hasInfluenced development of deep Q-networks
standard RL benchmarking practices
inputType raw game screen pixels
license open source
operatingSystem cross-platform
outputType discrete game actions
platformType game-based AI benchmark
provides common benchmark tasks
reproducible experimental setup
standardized environment API
providesInterfaceTo Atari 2600 games
researchArea control under uncertainty
sequential decision making
supports hundreds of Atari 2600 games
supportsFeature deterministic game modes
frame skipping
game state inspection
game state saving and loading
reward signal access
stochastic game modes
supportsTask deep reinforcement learning
imitation learning experiments
policy-based reinforcement learning
value-based reinforcement learning
timeScale real-time game simulation
usedBy machine learning researchers
reinforcement learning practitioners
usedFor benchmarking AI agents
developing reinforcement learning algorithms
evaluating reinforcement learning algorithms
usedIn academic research papers
algorithm comparison studies

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