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/rohitrango/Reward-bias-in-GAIL

Generative Adversarial Imitation Learning

https://github.com/romi514/TLSGAN

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

https://github.com/ronakdm/irl-text-generation

Toward Diverse Text Generation with Inverse Reinforcement Learning

https://github.com/roop-pal/Meta-Learning-for-StarCraft-II-Minigames

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/roythuly/obac

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

https://github.com/rperezdattari/Interactive-Learning-with-Corrective-Feedback-for-Policies-based-on-Deep-Neural-Networks

Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

https://github.com/rrags/ProtoX_NeurIPS

ProtoX: Explaining a Reinforcement Learning Agent via Prototyping

https://github.com/rshah240/conv_visualiser

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

https://github.com/rshnn/battleship

Proximal Policy Optimization Algorithms

https://github.com/rsilveira79/intents_uncertainty

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

https://github.com/ruotianluo/GoogleConceptualCaptioning

Self-critical Sequence Training for Image Captioning

https://github.com/ruotianluo/ImageCaptioning.pytorch

Self-critical Sequence Training for Image Captioning

https://github.com/ruotianluo/Transformer_Captioning

Self-critical Sequence Training for Image Captioning

https://github.com/ruotianluo/neuraltalk2.pytorch

Self-critical Sequence Training for Image Captioning

https://github.com/ruotianluo/self-critical.pytorch

Self-critical Sequence Training for Image Captioning

https://github.com/rupes438/CodeGen

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/rutgersdm/dkgr

Learning to Walk with Dual Agents for Knowledge Graph Reasoning

https://github.com/rvl-lab-utoronto/HyperCRL

Continual Model-Based Reinforcement Learning with Hypernetworks

https://github.com/rwang0417/d2c_mujoco200

Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems

https://github.com/rwbfd/OpenCompetitionV2

Searching for A Robust Neural Architecture in Four GPU Hours

https://github.com/ryan-dorazio/mmd-dilated

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

https://github.com/ryanjulian/embed2learn

Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing

https://github.com/ryanp73/ChessAI

Giraffe: Using Deep Reinforcement Learning to Play Chess

https://github.com/rybread1/DeepRlTrex

Deep Reinforcement Learning with Double Q-learning

https://github.com/rybread1/deep-rl-trex

Deep Reinforcement Learning with Double Q-learning

https://github.com/ryoasu/grad-cam

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

https://github.com/s-52/ImageCaptions

Self-critical Sequence Training for Image Captioning

https://github.com/s-a-malik/multi-few

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

https://github.com/s-sd/task-amenability

Continuous control with deep reinforcement learning

https://github.com/sabeaussan/ROS_Unity

Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization

https://github.com/sachink2010/AutomatedStockTrading-DeepQ-Learning

Financial Trading as a Game: A Deep Reinforcement Learning Approach

https://github.com/sadhikamalladi/RL-Coloring

Learning Combinatorial Optimization Algorithms over Graphs

https://github.com/safe-rl-team/viper-verifiable-rl-impl

Verifiable Reinforcement Learning via Policy Extraction

https://github.com/sagpant/ViZDoom

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

https://github.com/sagpant/leela-zero

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

https://github.com/saha0073/Deep-Reinforcement-Learning-to-play-Cartpole

Playing Atari with Deep Reinforcement Learning

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