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/moserle/TSJS-Grad-CAM

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

https://github.com/mostafachatillon/ChemGAN-challenge

ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

https://github.com/mpatacchiola/gym-multigoal

Reinforcement Learning with Deep Energy-Based Policies

https://github.com/mpatacchiola/imujoco

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/mrahtz/learning-from-human-preferences

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

https://github.com/mrklees/deepconv-chess

Giraffe: Using Deep Reinforcement Learning to Play Chess

https://github.com/msandeep27/Pytorch-Grad-Cam

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

https://github.com/mtrazzi/two-step-task

Learning to reinforcement learn

https://github.com/mugoh/rl-base/tree/master/rlbase/aiRL

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/mugoh/rl-base/tree/master/rlbase/d2rl

D2RL: Deep Dense Architectures in Reinforcement Learning

https://github.com/muriloime/awesome-stars

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/murtazarang/MD-MADDPG

Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communication

https://github.com/muttimirco/memento

Unsupervised Reinforcement Learning in Multiple Environments

https://github.com/muupan/async-rl

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/mvaldenegro/keras-uncertainty

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

https://github.com/mwalton/openspiel_benchmarks

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/mwbrulhardt/penv

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/mxblr/DeepRLHockey

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

https://github.com/mykolesiko/nlp_projects

Self-critical Sequence Training for Image Captioning

https://github.com/myungsub/meta-interpolation

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

https://github.com/mzhao98/DeepQLearning_CrossyRoad

Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics

https://github.com/mzymzy/paper3-quantized_distillzation

Model compression via distillation and quantization

https://github.com/n-akram/GradCamDDD

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

https://github.com/n0o8o0n1um/Gym1

OpenAI Gym

https://github.com/n1shetty/Cart-Pole-Balance-with-Reinforcement-Learning

Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem

https://github.com/n9839950/EGH400-2-DSN

Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

https://github.com/nagaban2/nn_dynamics

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/nalbarr/coursera-ai4med-course3

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

https://github.com/nandomp/AICollaboratory

The Arcade Learning Environment: An Evaluation Platform for General Agents

https://github.com/nanzhaogang/contrib/tree/master/application/translating-math-formula-images-to-labtex-sequences-using-deep-neural-networks-with-sequence-level-training

Translating Math Formula Images to LaTeX Sequences Using Deep Neural Networks with Sequence-level Training

https://github.com/narutomst/Auto-CNN-HSI-Classification

DARTS: Differentiable Architecture Search

https://github.com/naruya/vgan-pytorch

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/nascarsayan/dl-rubiks-autodidactic-solver

Solving the Rubik's Cube Without Human Knowledge

https://github.com/nataliemaus/joco_icml24

Scalable Global Optimization via Local Bayesian Optimization

https://github.com/nathangrinsztajn/Box-World

Relational Deep Reinforcement Learning

https://github.com/nathangrinsztajn/rubiks_cube

Solving the Rubik's Cube Without Human Knowledge

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