Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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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/backgom2357/Recommender_system_via_deep_RL

Continuous control with deep reinforcement learning

https://github.com/bastienvanderplaetse/action-branching-dqn

Action Branching Architectures for Deep Reinforcement Learning

https://github.com/baturaysaglam/RIS-MISO-Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/baturaysaglam/TCD3

Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods

https://github.com/baturaysaglam/ris-miso-pda-deep-reinforcement-learning

Deep Intrinsically Motivated Exploration in Continuous Control

https://github.com/bay3s/ppo-parallel

Proximal Policy Optimization Algorithms

https://github.com/bay3s/ppo-rl

Proximal Policy Optimization Algorithms

https://github.com/benevolentAI/guacamol_baselines

Proximal Policy Optimization Algorithms

https://github.com/benpetit/cs379c

Proximal Policy Optimization Algorithms

https://github.com/bentrevett/pytorch-rl

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/bidimensional/Tennis

Continuous control with deep reinforcement learning

https://github.com/biemann/Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/billchan226/poar-srl-4-robot

Simple random search provides a competitive approach to reinforcement learning

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

Continuous control with deep reinforcement learning

https://github.com/bmeyers/VirtualMicrogridSegmentation

Continuous control with deep reinforcement learning

https://github.com/bonniesjli/PPO-Reacher_UnityML

Proximal Policy Optimization Algorithms

https://github.com/bonniesjli/PPO_Reacher

Proximal Policy Optimization Algorithms

https://github.com/brain-research/mirage-rl

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

https://github.com/brandontrabucco/efficient-hrl

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

https://github.com/bstadie/third_person_im

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/buoyancy99/sap

Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation

https://github.com/camall3n/atari-MAC

Mean Actor Critic

https://github.com/cardwing/Codes-for-Steering-Control

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

https://github.com/cataluna84/WorldModels_v1.0

World Models

https://github.com/catalyst-team/catalyst-rl-framework

Catalyst.RL: A Distributed Framework for Reproducible RL Research

https://github.com/cathywu/rllab-multiagent

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/cav-research-lab/predictive-model-delay-correction

Reinforcement Learning with Random Delays

https://github.com/cbfinn/maml_rl

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/ccolas/rl_stats

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

https://github.com/chainer/chainerrl

Continuous control with deep reinforcement learning

https://github.com/chamorajg/pl-dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/chandar-lab/LoCA2

Learning Latent Dynamics for Planning from Pixels

https://github.com/changmin-yu/Prediction_and_Generalisation_over_Directed_Actions_by_Grid_Cells

Prediction and Generalisation over Directed Actions by Grid Cells

https://github.com/chinganc/lightatac

Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/choyingw/Voice2Mesh

Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?

https://github.com/cindycia/Atari-SAC-Discrete

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

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