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/dmksjfl/smr

Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse

https://github.com/dmund95/Off-Policy-Deep-RL

Trajectory-Based Off-Policy Deep Reinforcement Learning

https://github.com/donamin/llc

Proximal Policy Optimization Algorithms

https://github.com/doty-k/world_models

World Models

https://github.com/downingbots/RLDonkeycar

Proximal Policy Optimization Algorithms

https://github.com/dpoulopoulos/drl_collaborate_compete

Continuous control with deep reinforcement learning

https://github.com/dpoulopoulos/drl_continuous_control

Continuous control with deep reinforcement learning

https://github.com/dsinghnegi/atari_RL_agent

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/dti-research/SenseAct

Benchmarking Reinforcement Learning Algorithms on Real-World Robots

https://github.com/dvij542/offtersim

Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

https://github.com/dyabel/handson_rl

Proximal Policy Optimization Algorithms

https://github.com/dyth/doublegum

Continuous control with deep reinforcement learning

https://github.com/edmundmills/basalt-competition

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/eladsar/rbi

Proximal Policy Optimization Algorithms

https://github.com/emerge-lab/nocturne_lab

Proximal Policy Optimization Algorithms

https://github.com/enikhu/dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/enpasos/muzero

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/eric-erki/robotics-rl-srl

Simple random search provides a competitive approach to reinforcement learning

https://github.com/ermongroup/higher_order_invariance

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/ethanluoyc/compile-jax

CompILE: Compositional Imitation Learning and Execution

https://github.com/evieq01/oodil

Proximal Policy Optimization Algorithms

https://github.com/facebookresearch/Horizon

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/ReAgent

Proximal Policy Optimization Algorithms

https://github.com/facebookresearch/denoised_mdp

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/facebookresearch/gwil

Cross-Domain Imitation Learning via Optimal Transport

https://github.com/facebookresearch/rl

Smooth Exploration for Robotic Reinforcement Learning

https://github.com/facebookresearch/rl/blob/main/examples/ddpg/ddpg.py

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/rl/blob/main/examples/sac/sac.py

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

https://github.com/facebookresearch/slbo

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

https://github.com/fdcl-gwu/gym-rotor

Continuous control with deep reinforcement learning

https://github.com/feruxhi/thoughts

Continuous control with deep reinforcement learning

https://github.com/feryal/craft-env

Modular Multitask Reinforcement Learning with Policy Sketches

https://github.com/fgitmichael/SVAEfromSLAC

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/fgitmichael/TestModeDisentangling

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/fhbzc/FishAgentSimulation

Continuous control with deep reinforcement learning

https://github.com/fiberleif/nc_ddpg

Continuous control with deep reinforcement learning

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