Library/baselines
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openai/baselines

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

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OpenAI

OpenAI

openai • ai-lab

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16,684

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4,947

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Created

May 24, 2017

Project creation date

README Summary

OpenAI Baselines is a set of high-quality implementations of reinforcement learning algorithms, meant to serve as a baseline to try on your problems. The repository provides well-tested, standardized implementations of popular RL algorithms like PPO, A2C, DQN, and others. These implementations are designed to be used as starting points for research and development in reinforcement learning.

AI Dev Skills

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Deep Reinforcement LearningPolicy Gradient MethodsQ-Learning AlgorithmsActor-Critic MethodsProximal Policy OptimizationTrust Region Policy OptimizationDeep Q-NetworksAdvantage Actor-CriticReinforcement Learning Algorithm Implementation

Tags

Deep Reinforcement LearningPolicy Gradient MethodsQ-Learning AlgorithmsActor-Critic MethodsProximal Policy OptimizationTrust Region Policy OptimizationDeep Q-NetworksAdvantage Actor-CriticReinforcement Learning Algorithm ImplementationSequential Decision MakingRoboticsCloud APIAgentic AIAutonomous NavigationGame AI TrainingTrading/FinTechImageAI SafetyRobot ControlSelf-hostedTabularResource AllocationPolicy LearningAutonomous VehiclesGamingPython

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Recent Activity

Updated 1 years ago

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Quality

research
Quality
high
Maturity
research

Categories

Industry: FinTechPrimarySafety & AlignmentFinance & LegalOther AI / MLAI AgentsModel TrainingRobotics

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Developer Platform

Languages

Python100.0%

Timeline

Project created
May 24, 2017
Forked
Mar 14, 2026
Your last push
1 years ago
Upstream last push
1 years ago
Tracked since
Aug 1, 2024

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