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OpenAI Gym

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평가표: 17
보존된 평가 기록: 45

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/LucasSilve/openAIgym

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/MLCS-Yonsei/ddpg-control

Continuous control with deep reinforcement learning

https://github.com/MarsEleven/car_racer_RL

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/MathPhysSim/PER-NAF

Continuous control with deep reinforcement learning

https://github.com/MatteoBrentegani/PPO

Proximal Policy Optimization Algorithms

https://github.com/MatthieuSarkis/Portfolio-Optimization-and-Goal-Based-Investment-with-Reinforcement-Learning

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Proximal_Policy_Optimization_Algorithms

Proximal Policy Optimization Algorithms

https://github.com/Medabid1/RL_Project

Continuous control with deep reinforcement learning

https://github.com/Michaelrising/Prog-RL

Proximal Policy Optimization Algorithms

https://github.com/MindCode-4/code-11/tree/main/comparing-the-efficacy

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation

https://github.com/MindCode-4/code-3/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/MindCode-4/code-6/tree/main/comparing-the-efficacy

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation

https://github.com/MindSpore-scientific/code-3/tree/main/comparing-the-efficacy

Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation

https://github.com/MishaLaskin/rad

Reinforcement Learning with Augmented Data

https://github.com/MrSyee/pg-is-all-you-need

Continuous control with deep reinforcement learning

https://github.com/NACLab/robust-active-inference

Proximal Policy Optimization Algorithms

https://github.com/NOHYC/autonomous_driving_car_project

Continuous control with deep reinforcement learning

https://github.com/Narsil/rl-baselines

Proximal Policy Optimization Algorithms

https://github.com/NervanaSystems/coach

Continuous control with deep reinforcement learning

https://github.com/NitinVishalKulkarni/OfflineReinforcementLearning

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/Nordeus/heroic-rl

Proximal Policy Optimization Algorithms

https://github.com/OctThe16th/PPO-Keras

Proximal Policy Optimization Algorithms

https://github.com/OctopusMind/RLHF_PPO

Proximal Policy Optimization Algorithms

https://github.com/Osrip/CriticalCoEvolution

Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents

https://github.com/Osrip/CriticalEvolution

Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents

https://github.com/Osrip/evolution_dynamical_regime

Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents

https://github.com/Ostyk/walk-bot

Proximal Policy Optimization Algorithms

https://github.com/Pechckin/MountainCar

Continuous control with deep reinforcement learning

https://github.com/PeterJochem/Deep_RL

Continuous control with deep reinforcement learning

https://github.com/PeterJochem/Double_Deep_QLearning

Continuous control with deep reinforcement learning

https://github.com/Philori22/DDPG-aigym

Continuous control with deep reinforcement learning

https://github.com/QuentinVacher-rl/SoftActorCritic-in-Cpp-using-LibTorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/RLAgent/state-marginal-matching

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/RPSR-DQN/paper-code

Recurrent Predictive State Policy Networks

https://github.com/Rafael1s/Deep-Reinforcement-Learning-Udacity

Proximal Policy Optimization Algorithms

https://github.com/Rajesh-Siraskar/RL_VC_CodeOcean

Reinforcement Learning for Control of Valves

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.