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/IngookJang/reinforcement-learning-TX2

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

https://github.com/InsaneMonster/pasqualini2020prngrl

Pseudo Random Number Generation: a Reinforcement Learning approach

https://github.com/IntelLabs/bayesian-torch

Weight Uncertainty in Neural Networks

https://github.com/Intelligent-Reliable-Autonomous-Systems/WOFOSTGym

CropGym: a Reinforcement Learning Environment for Crop Management

https://github.com/Ipsedo/EvoMotion

Proximal Policy Optimization Algorithms

https://github.com/Ipsedo/MARLClassification

Multi-Agent Image Classification via Reinforcement Learning

https://github.com/Ishan-Kumar2/Reinforcement-Learning-on-2048

Playing Atari with Deep Reinforcement Learning

https://github.com/IsmaelCesar/darts

DARTS: Differentiable Architecture Search

https://github.com/IvanVigor/Deep-Deterministic-Policy-Gradient-Unity-Env

Continuous control with deep reinforcement learning

https://github.com/IvanVigor/MADDPG-Unity

Continuous control with deep reinforcement learning

https://github.com/Ivsxk/RAT

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/J93T/TP4-DDPG

Continuous control with deep reinforcement learning

https://github.com/JBGUIMBAUD/deep-reenforcement-learning

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/JBLanier/distributed-rl-for-imperfect-info-games

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

https://github.com/JIElite/AI-research-papers

Deep Reinforcement Learning for Playing 2.5D Fighting Games

https://github.com/JL321/Proximal-Policy-Optimization

Proximal Policy Optimization Algorithms

https://github.com/JL321/mujo-2DHalf-Cheetah

Continuous control with deep reinforcement learning

https://github.com/JL321/mujo-2DWalker

Continuous control with deep reinforcement learning

https://github.com/JRC1995/Abstractive-Summarization

A Deep Reinforced Model for Abstractive Summarization

https://github.com/JWSoh/MZSR

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

https://github.com/JackFurby/Breakout

Playing Atari with Deep Reinforcement Learning

https://github.com/JacobM184/EfficientNet-for-Gun-detection

Searching for Activation Functions

https://github.com/JacopoPan/gym-pybullet-drones

Flightmare: A Flexible Quadrotor Simulator

https://github.com/JanghoonChoi/janghoonchoi.github.io

Real-time visual tracking by deep reinforced decision making

https://github.com/Jara555/CNN-for-AD-classification

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

https://github.com/JasperBusschers/multi-objective-Rubik-s-cube

Solving the Rubik's Cube Without Human Knowledge

https://github.com/JayLohokare/banana-world-navigation

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/JayLohokare/taxi-v2

Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

https://github.com/Jekyll1021/RARL

Robust Adversarial Reinforcement Learning

https://github.com/JeonMinkyu/MAML_Pytorch

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

https://github.com/Ji4chenLi/ARS_experiment

Simple random search provides a competitive approach to reinforcement learning

https://github.com/Jia-Research-Lab/GridMask

GridMask Data Augmentation

https://github.com/JianGuanTHU/RecGAN

Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation

https://github.com/JiazhengChai/synergyDRL

Soft Actor-Critic Algorithms and Applications

https://github.com/JinTanda/MADDPG_env

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/Jingliang-Duan/Distributional-Soft-Actor-Critic

Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

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