Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

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코드

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

https://github.com/CSautier/Breakout

Proximal Policy Optimization Algorithms

https://github.com/CSautier/PongBot

Proximal Policy Optimization Algorithms

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/ChristosKap/policy_consolidation

Policy Consolidation for Continual Reinforcement Learning

https://github.com/Crevass/Hybrid-Agent

Continuous control with deep reinforcement learning

https://github.com/DHDev0/Muzero-unplugged

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/DMIU-ShELL/MOSAIC

Proximal Policy Optimization Algorithms

https://github.com/Dam930/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/DamienLancry/blocking_torcs

Simulated Car Racing Championship: Competition Software Manual

https://github.com/DamienLancry/torcs-1.3.4

Simulated Car Racing Championship: Competition Software Manual

https://github.com/DanielLSM/safe-rl-tutorial

Continuous control with deep reinforcement learning

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/Dascka/dl_q-learning_comparator

Deep Reinforcement Learning that Matters

https://github.com/DataScienceNigeria/D-Kitty-D-Claw.-Robotics-Benchmarks-for-Learning-with-Low-Cost-Robots-

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

https://github.com/DavidCastilloAlvarado/Path-planning-and-Reinforcement-Learning

Proximal Policy Optimization Algorithms

https://github.com/Deepest-Project/WorldModels-A3C

World Models

https://github.com/Dekki-Aero/DDPG

Continuous control with deep reinforcement learning

https://github.com/DevSlem/AINE-DRL

Proximal Policy Optimization Algorithms

https://github.com/Driesssens/ibp-pytorch

Learning model-based planning from scratch

https://github.com/Eclectic-Sheep/sheeprl

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/EconomistGrant/HTFE-tensortrade

Proximal Policy Optimization Algorithms

https://github.com/EndingCredits/gym

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

https://github.com/EyaRhouma/collaboration-competition-MADDPG

Continuous control with deep reinforcement learning

https://github.com/FMArduini/python-rl

Proximal Policy Optimization Algorithms

https://github.com/FOCAL-ICLR/FOCAL-ICLR

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

https://github.com/Fable67/Streamlined-Off-Policy-Learning

Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

https://github.com/FlyienSHaDOw/continuous_control

Continuous control with deep reinforcement learning

https://github.com/FlyienSHaDOw/project_2_continuous_control

Continuous control with deep reinforcement learning

https://github.com/GauravPatel89/Car-Navigation-Simulation-using-TD3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/GiadaSimionato/Reasoning_Agents_2020

Proximal Policy Optimization Algorithms

https://github.com/Gouet/Acrobot-PPO

Proximal Policy Optimization Algorithms

https://github.com/Gouet/Breakout-V0

Proximal Policy Optimization Algorithms

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