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/sahandrez/gym_forestfire

OpenAI Gym

https://github.com/saikrishna-1996/deep_pepper_chess

Giraffe: Using Deep Reinforcement Learning to Play Chess

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

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

https://github.com/sainijagjit/A3C-Pytorch

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/salesforce/codet5

CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

https://github.com/samadiamir/IE2E

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

https://github.com/samhippie/q-game-agent

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/samiamkhan/4641Project

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/samiranrl/ODSC_2019_RL

Continuous control with deep reinforcement learning

https://github.com/samson6460/tf-keras-gradcamplusplus

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

https://github.com/samson6460/tf.keras-gradcamplusplus

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

https://github.com/samson6460/tf_keras_gradcamplusplus

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

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

World Models

https://github.com/samuelfneumann/GoAtar

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

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

Continuous control with deep reinforcement learning

https://github.com/samzhang111/arxiv-covid

Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning

https://github.com/sandeeprockstar/Pose_Estimation

An Empirical Model of Large-Batch Training

https://github.com/sannebh/riskaverse

Distributional Reinforcement Learning with Quantile Regression

https://github.com/sannebh/riskaverse_rl

Distributional Reinforcement Learning with Quantile Regression

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

Continuous control with deep reinforcement learning

https://github.com/sapanachaudhary/pytorch-cpo

Constrained Policy Optimization

https://github.com/sarahisyoung/rlpyt

Accelerated Methods for Deep Reinforcement Learning

https://github.com/sarmueller/gibo

Simple random search provides a competitive approach to reinforcement learning

https://github.com/sarthakTUM/progressive-neural-networks-for-nlp

Progressive Neural Networks

https://github.com/sarus-tech/tf2-published-models/tree/master/pixelsnail

PixelSNAIL: An Improved Autoregressive Generative Model

https://github.com/saschaschramm/Pong

Proximal Policy Optimization Algorithms

https://github.com/sauravmishra1710/EXPLAINABLE-AI---Skin-Cancer-Detection-explained-with-GRADCAM

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

https://github.com/sauravmishra1710/Malaria-Detection-Using-Deep-Learning-Techniques

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

https://github.com/sauxpa/ML_101

Temporal Regularization in Markov Decision Process

https://github.com/sauxpa/neural_exploration

Provably Efficient Reinforcement Learning with Linear Function Approximation

https://github.com/saxena-mayur/Weight-Uncertainty-in-Neural-Networks

Weight Uncertainty in Neural Networks

https://github.com/saya1984/MonsterCarlo2

Unity: A General Platform for Intelligent Agents

https://github.com/sayantanauddy/hierarchical_bipedal_controller

Continuous control with deep reinforcement learning

https://github.com/sb-ai-lab/RePlay

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

https://github.com/sbieringer/classificationsurrogates

Weight Uncertainty in Neural Networks

https://github.com/sbodenstein/Tutorials

Simple random search provides a competitive approach to reinforcement learning

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