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/rlworkgroup/garage

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/robintyh1/icml2021-pengqlambda

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/roythuly/obac

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

https://github.com/rperezdattari/Interactive-Learning-with-Corrective-Feedback-for-Policies-based-on-Deep-Neural-Networks

Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

https://github.com/rshnn/battleship

Proximal Policy Optimization Algorithms

https://github.com/runamu/compositional-conservatism

MOPO: Model-based Offline Policy Optimization

https://github.com/russellmendonca/maesn_suite

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/s-sd/task-amenability

Continuous control with deep reinforcement learning

https://github.com/safe-slac/safe-slac

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

https://github.com/sail-sg/offbench

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/samiranrl/ODSC_2019_RL

Continuous control with deep reinforcement learning

https://github.com/samuel500/World-Models

World Models

https://github.com/samuelmat19/DDPG-tf2

Continuous control with deep reinforcement learning

https://github.com/santolucito/Haskell-TORCS

Simulated Car Racing Championship: Competition Software Manual

https://github.com/saoudh/Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/sarmueller/gibo

Simple random search provides a competitive approach to reinforcement learning

https://github.com/saschaschramm/Pong

Proximal Policy Optimization Algorithms

https://github.com/sayantanauddy/hierarchical_bipedal_controller

Continuous control with deep reinforcement learning

https://github.com/sbodenstein/Tutorials

Simple random search provides a competitive approach to reinforcement learning

https://github.com/sc2crazy/StarCrackRL

Proximal Policy Optimization Algorithms

https://github.com/schatty/D4PG-pytorch

Continuous control with deep reinforcement learning

https://github.com/scottemmons/youngs-cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/scoutant/face-blur

Towards Fast, Accurate and Stable 3D Dense Face Alignment

https://github.com/serengil/deepface

https://github.com/seungjaeryanlee/osim-rl-helper

Continuous control with deep reinforcement learning

https://github.com/seungju-k1m/sac-td3-td7

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

https://github.com/sgoldyaev/DeepFashion.ADGAN

Controllable Person Image Synthesis with Attribute-Decomposed GAN

https://github.com/shahabi8/Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/shahin-01/vqa-ad

Continuous control with deep reinforcement learning

https://github.com/shakedzy/warehouse

Continuous control with deep reinforcement learning

https://github.com/shamanez/Variational-Discriminator-Bottleneck-Tensorflow-Implementation

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/shehrum/RL_Continous-Control

Continuous control with deep reinforcement learning

https://github.com/shinian123/gym

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

https://github.com/shiningsunnyday/mcts-chess

Proximal Policy Optimization Algorithms

https://github.com/shreyasc-13/off_domain_rlkit

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

https://github.com/shreyesss/PPO-implementation-keras-tensorflow

High-Dimensional Continuous Control Using Generalized Advantage Estimation

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