Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

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/yihaosun1124/OfflineRL-Kit

MOPO: Model-based Offline Policy Optimization

https://github.com/yimingpeng/sac-master

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

https://github.com/yining043/SAC-discrete

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

https://github.com/yoavalon/Quadcopter-env

Proximal Policy Optimization Algorithms

https://github.com/yoavalon/QuadcopterReinforcementLearning

Proximal Policy Optimization Algorithms

https://github.com/young-geng/cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/young-geng/jaxcql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/yueqiw/gqn-world-model

World Models

https://github.com/yukezhu/tensorflow-reinforce

Continuous control with deep reinforcement learning

https://github.com/yusme/DDPG

Continuous control with deep reinforcement learning

https://github.com/yusukeurakami/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/yusukeurakami/slac-playground

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

https://github.com/yycdavid/program-synthesis-guided-rl

World Models

https://github.com/yydsok/oparl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/zacwellmer/worldmodels

World Models

https://github.com/zahinsufiyan/flow-networks-fault-adaptation

CFlowNets: Continuous Control with Generative Flow Networks

https://github.com/zbzhu99/madiff

Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification

https://github.com/zhaoyi11/tcrl

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/zhixuan-lin/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/zhou-henry/distributed-distributional-drq

Distributed Distributional Deterministic Policy Gradients

https://github.com/ziyizhang27/sdpo

REBEL: Reinforcement Learning via Regressing Relative Rewards

https://github.com/zjlab-ammi/llm4rl

Proximal Policy Optimization Algorithms

https://github.com/zsz-hst/RL_single_chase_point

Proximal Policy Optimization Algorithms

https://github.com/zubair-irshad/imitation_learning

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

https://github.com/zzmtsvv/rl_task

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

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