Papers with Code 아카이브
OpenAI Gym
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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An open-source toolkit from OpenAI that implements several Reinforcement Learning benchmarks including: classic control, Atari, Robotics and MuJoCo tasks.
(Description by Evolutionary learning of interpretable decision trees)
(Image Credit: OpenAI Gym)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Aravind-11/IITM_Saastra
https://github.com/Aravind-11/Multi-Agent-RL
https://github.com/AutumnWu/Streamlined-Off-Policy-Learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/TD3Agent.py
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/BY571/Soft-Actor-Critic-and-Extensions
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/Baichenjia/BHER
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
https://github.com/Bam4d/GVGAI_GYM
https://github.com/Bam4d/Neural-Game-Engine
Neural Game Engine: Accurate learning of generalizable forward models from pixels
https://github.com/BerkeleyLearnVerify/VerifAI
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/Brook1711/RIS_components
https://github.com/CSautier/Breakout
https://github.com/CSautier/PongBot
https://github.com/CharlotteMorrison/Baxter-Research
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/CharlotteMorrison/Baxter-VREP
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/CharlotteMorrison/Baxter-VREP-Version-2
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/Christopheraburns/openai-gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/Crevass/Hybrid-Agent
https://github.com/DLR-RM/stable-baselines3
https://github.com/DMIU-ShELL/MOSAIC
https://github.com/DanielLSM/safe-rl-tutorial
https://github.com/DanielTakeshi/DCUR
Addressing Function Approximation Error in Actor-Critic Methods
https://github.com/DartEnv/dart-env
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/DavidCastilloAlvarado/Path-planning-and-Reinforcement-Learning
https://github.com/Dekki-Aero/DDPG
https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/DevSlem/AINE-DRL
https://github.com/EconomistGrant/HTFE-tensortrade
https://github.com/EndingCredits/gym
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
https://github.com/EyaRhouma/collaboration-competition-MADDPG
https://github.com/FMArduini/python-rl
https://github.com/FOCAL-ICLR/FOCAL-ICLR
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
https://github.com/FlyienSHaDOw/continuous_control
https://github.com/FlyienSHaDOw/project_2_continuous_control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.