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openai/gym

gym_

A toolkit for developing and comparing reinforcement learning algorithms.

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OpenAI

OpenAI

openai • ai-lab

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Apr 27, 2016

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README Summary

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms that provides a standardized API for various environments. It offers a wide collection of test problems called environments that expose a common interface, allowing researchers to write general algorithms that can be applied across different tasks. The toolkit serves as a benchmark suite for reinforcement learning research and enables easy comparison of different RL approaches.

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Reinforcement LearningAlgorithm BenchmarkingEnvironment SimulationPolicy LearningAction-Value MethodsDeep Q-LearningPolicy Gradient MethodsActor-Critic MethodsMulti-Agent Reinforcement LearningMarkov Decision Processes

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Reinforcement LearningAlgorithm BenchmarkingEnvironment SimulationPolicy LearningAction-Value MethodsDeep Q-LearningPolicy Gradient MethodsActor-Critic MethodsMulti-Agent Reinforcement LearningMarkov Decision ProcessesFinanceSequential Decision MakingVisual ObservationsLocal DevelopmentNumerical State VectorsDiscrete Action SpacesResearch InfrastructureSelf-hostedRoboticsHealthcareAgentic AIRobotic Sensor DataCloud TrainingMulti-Agent SystemsAutonomous VehiclesControl System OptimizationGamingGame AI DevelopmentBenchmark ComparisonContinuous Action SpacesAlgorithm Development and TestingAI SafetyAgent Training and EvaluationAutonomous Decision MakingEducational RL ExperimentsReinforcement Learning ResearchSimulation-to-Reality TransferAtari Game FramesPython

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Project created
Apr 27, 2016
Forked
Mar 14, 2026
Your last push
1 years ago
Upstream last push
18 days ago
Tracked since
Oct 11, 2024

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