Papers with Code 아카이브

Reinforcement Learning

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코드

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

https://github.com/nolanwinsman/Team-Doom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/nolisten/erl

Count-Based Exploration with Neural Density Models

https://github.com/nomercy77/Implementing-Bayesian-CNN

Weight Uncertainty in Neural Networks

https://github.com/norse/norse

Long short-term memory and learning-to-learn in networks of spiking neurons

https://github.com/notdibya/gcsl

Learning to Reach Goals via Iterated Supervised Learning

https://github.com/notem/keras-alexnet

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

https://github.com/nouwaarom/ml-agents

Unity: A General Platform for Intelligent Agents

https://github.com/novice03/timm-vis

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

https://github.com/novicestone/meta-cetm

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/nric/ProximalPolicyOptimizationContinuousKeras

Proximal Policy Optimization Algorithms

https://github.com/nurpeiis/LeakGAN-PyTorch

Long Text Generation via Adversarial Training with Leaked Information

https://github.com/nutellamok/advrush

DARTS: Differentiable Architecture Search

https://github.com/nwpuhkp/DRL-Backdoor-Pong

TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents

https://github.com/nyu-dl/dl4ir-query-reformulator

Task-Oriented Query Reformulation with Reinforcement Learning

https://github.com/nyuolab/VIPER-torch

Video Prediction Models as Rewards for Reinforcement Learning

https://github.com/obastani/model-predictive-shielding

Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive Shielding

https://github.com/oceanypt/A-DEEP-REINFORCED-MODEL-FOR-ABSTRACTIVE-SUMMARIZATION

A Deep Reinforced Model for Abstractive Summarization

https://github.com/octothrope/ARS

Simple random search provides a competitive approach to reinforcement learning

https://github.com/odokumaci/rainbow-unity-obstacle-tower-challenge

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/oereo/ML_Agents_App

Unity: A General Platform for Intelligent Agents

https://github.com/ofekluis/sonic_project_ss19

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/ofsoundof/Heterogeneity_Hypothesis

DHP: Differentiable Meta Pruning via HyperNetworks

https://github.com/olivesgatech/explanatory-paradigms

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

https://github.com/olivesgatech/introspective-learning

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

https://github.com/oliviawl/image_classification_utkface

A Study on Overfitting in Deep Reinforcement Learning

https://github.com/ollenilsson19/MAP-Elites-GAPG

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ollenilsson19/PGA-MAP-Elites

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/olonok69/Udacity_Banana_Unity

Prioritized Experience Replay

https://github.com/om00839/machine-suneung

Stochastic Answer Networks for Machine Reading Comprehension

https://github.com/omacshane/basicbayesDL

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

https://github.com/omkarv/pong-from-pixels

Playing Atari with Deep Reinforcement Learning

https://github.com/omron-sinicx/multipolar

MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics

https://github.com/onlytailei/Value-Iteration-Networks-PyTorch

Value Iteration Networks

https://github.com/open-dynamic-robot-initiative/robot_fingers

TriFinger: An Open-Source Robot for Learning Dexterity

https://github.com/open-dynamic-robot-initiative/trifinger_simulation

TriFinger: An Open-Source Robot for Learning Dexterity

https://github.com/open-mmlab/mmdetection

NAS-FCOS: Fast Neural Architecture Search for Object Detection

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