Papers with Code 아카이브

Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

162

보존된 평가 기록

1

코드

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

https://github.com/Dungyichao/Electric-Vehicle-Route-Planning-on-Google-Map-Reinforcement-Learning

Value Iteration Networks

https://github.com/EACN2022/EACN

Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Weight Uncertainty in Neural Networks

https://github.com/EVavylonis/iSEER

Unity: A General Platform for Intelligent Agents

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/EdNiehues/simulacao-de-projeto-para-mapeamento

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/ElectricAlexis/NotaGen

NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigms

https://github.com/ElisevanderPol/symmetrizer

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

https://github.com/Eljefemasao/Customed-Object-Detector

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Eljefemasao/MarkNet-ObjectDetector

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/ElleryL/gradient_estimator

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

https://github.com/EndingCredits/gym

OpenAI Gym

https://github.com/EpiSci/SoCRATES

DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling

https://github.com/Epsilon10/MARL

Soft Actor-Critic Algorithms and Applications

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/autoregressive/pixel_snail.py

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/Eungyu-Jin/Dropout_uncertainty

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/Evolving-AI-Lab/cmoea

Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm

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

Continuous control with deep reinforcement learning

https://github.com/FJSam/SelfCritical_ImageCaptioning

Self-critical Sequence Training for Image Captioning

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

Proximal Policy Optimization Algorithms

https://github.com/FaboNo/DRLND

Deep Reinforcement Learning with Double Q-learning

https://github.com/FauzaanQureshi/deep-Q-learning

Playing Atari with Deep Reinforcement Learning

https://github.com/FenHua/SVA

Sparse Black-box Video Attack with Reinforcement Learning

https://github.com/Finspire13/RL-Surgical-Gesture-Segmentation

Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification

https://github.com/FlixCoder/rust-es-optimizer

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

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/Frager/Deep-Neuroevolution-In-SC2LE

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/FragileTech/FractalAI

Fractal AI: A fragile theory of intelligence

https://github.com/Fragjacker/Pointcloud-grad-CAM

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/Francesco-Sovrano/Combining--experience-replay--with--exploration-by-random-network-distillation-

Exploration by Random Network Distillation

https://github.com/FrancescoSaverioZuppichini/A-journey-into-Convolutional-Neural-Network-visualization-

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/FrankPortman/stannis

Monte Carlo Q-learning for General Game Playing

https://github.com/Fritschek/Reinforcement-Learning-for-Wireless

End-to-End Learning of Communications Systems Without a Channel Model

https://github.com/FudanNLP/Irl_gen

Toward Diverse Text Generation with Inverse Reinforcement Learning

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