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Reinforcement Learning

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코드

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

https://github.com/crowdAI/marLo

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

https://github.com/cshannonn/blackscholes_nas

Neural Architecture Search with Reinforcement Learning

https://github.com/csieber/pydashsim

Towards Machine Learning-Based Optimal HAS

https://github.com/csxeba/Reproducing-Q-AGREL

A Biologically Plausible Learning Rule for Deep Learning in the Brain

https://github.com/csyanbin/MAML-Pytorch-Multi-GPUs

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

https://github.com/ctallec/continuous-rl

Making Deep Q-learning methods robust to time discretization

https://github.com/ctallec/world-models

World Models

https://github.com/cts198859/deeprl_dist

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/cuijiaxun/MITC-Project

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/cww97/visual-language-grasping

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

https://github.com/cyanrain7/trpo-in-marl

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

https://github.com/cycraig/gym-goal

Reinforcement Learning with Parameterized Actions

https://github.com/cycraig/gym-platform

Reinforcement Learning with Parameterized Actions

https://github.com/cyoon1729/Multi-agent-reinforcement-learning

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

https://github.com/cyrusneary/rm-cooperative-marl

Reward Machines for Cooperative Multi-Agent Reinforcement Learning

https://github.com/czen88/qtrader

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/czgdp1807/bayesiandl

Weight Uncertainty in Neural Networks

https://github.com/czh513/Hierarchical-Critics-Assignment-for-Multi-agent-Reinforcement-Learning

Reinforcement Learning from Hierarchical Critics

https://github.com/czh513/RL-Hierarchical-Critics

Reinforcement Learning from Hierarchical Critics

https://github.com/czh513/Reinforcement-Learning-from-Hierarchical-Critics

Reinforcement Learning from Hierarchical Critics

https://github.com/d-eremeev/ADM-VRP

A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems

https://github.com/daggertye/GAN-Q-Learning

GAN Q-learning

https://github.com/daixiangzi/Grad_Cam-pytorch-resnet50

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

https://github.com/daje0601/Google_SCoRe

Training Language Models to Self-Correct via Reinforcement Learning

https://github.com/dajiangsuo/EV_mixtedAutonomy

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/damedollaforthree/nrml

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

https://github.com/dan-lennox/ml-udacity-quadcopter-rl

Continuous control with deep reinforcement learning

https://github.com/dangwalp/qg

Machine Comprehension by Text-to-Text Neural Question Generation

https://github.com/danielkelshaw/ConcreteDropout

Concrete Dropout

https://github.com/danielkelshaw/RadialBNN

Weight Uncertainty in Neural Networks

https://github.com/danielkelshaw/WeightUncertainty

Weight Uncertainty in Neural Networks

https://github.com/daniellawson9999/forwards_rl

End-to-End Robotic Reinforcement Learning without Reward Engineering

https://github.com/danielpolimac/Ispit_Inteligentni_Agenti

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/danielwillemsen/PendulumDemo

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/danielytan/doom_DQN

A Deeper Look at Experience Replay

https://github.com/danifuertes/top_transformer

Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning

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