Papers with Code 아카이브

3D Face Modelling

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zwfightzw/Meta-Critic

공식 코드

Online Meta-Critic Learning for Off-Policy Actor-Critic Methods

https://bitbucket.org/act-lab/release

Proximal Policy Optimization Algorithms

https://gitee.com/gai-shaoyan/mind3d

Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

https://github.com/09jvilla/CS234_gym

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

https://github.com/1110589721/openia-gym

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

https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning

Proximal Policy Optimization Algorithms

https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms

Proximal Policy Optimization Algorithms

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDAG

Continuous control with deep reinforcement learning

https://github.com/2mawi2/master-thesis-experiments

Proximal Policy Optimization Algorithms

https://github.com/62subinh/deeprl_safety_specification

Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach

https://github.com/ASzot/ppo-pytorch

Proximal Policy Optimization Algorithms

https://github.com/AdeelMufti/WorldModels

World Models

https://github.com/AgrawalAmey/safe-explorer

Continuous control with deep reinforcement learning

https://github.com/AlexZhaoZt/Temporal_Leap_HRL

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

https://github.com/AlexandraFilimokhina/MountainCarConrinious_DDPG

Continuous control with deep reinforcement learning

https://github.com/AliengirlLiv/xrl-dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/AmmarFayad/Behavioral-Actor-Critic

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

https://github.com/AndrewJWashington/protodriver

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/AnshMittal1811/AugmentedRandomSearch

Simple random search provides a competitive approach to reinforcement learning

https://github.com/AppliedDataSciencePartners/WorldModels

World Models

https://github.com/Aravind-11/AI-Gaming

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/IITM_Saastra

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/Multi-Agent-RL

Proximal Policy Optimization Algorithms

https://github.com/Asap7772/understanding-rlhf

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/AurelianTactics/bcq_tensorflow

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/B-C-WANG/ReinforcementLearningInAutoPilot

World Models

https://github.com/BFAnas/BranchingDQN_keras

Action Branching Architectures for Deep Reinforcement Learning

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py

Proximal Policy Optimization Algorithms

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/CQL

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/BY571/Normalized-Advantage-Function-NAF-

Continuous Deep Q-Learning with Model-based Acceleration

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

ARCHER: Aggressive Rewards to Counter bias in Hindsight Experience Replay

https://github.com/BerkeleyLearnVerify/VerifAI

Proximal Policy Optimization Algorithms

https://github.com/BrianPulfer/PapersReimplementations

Proximal Policy Optimization Algorithms

https://github.com/Brook1711/RIS_components

Continuous control with deep reinforcement learning

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