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Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/xuzhiyuan1528/KTM-DRL

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

https://github.com/xyshadow/baseline_ddpg

Continuous control with deep reinforcement learning

https://github.com/xzb030/img_survey

Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approach

https://github.com/xzxzxzxz/interp

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

https://github.com/yadrimz/option-critic

The Option-Critic Architecture

https://github.com/yangyucheng000/University/tree/main/model-1/qdqbert

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

https://github.com/yannbouteiller/gym-airsimdroneracinglab

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

https://github.com/yannbouteiller/rtgym

Reinforcement Learning with Random Delays

https://github.com/yanpei18345156216/BayesianOptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/yaof20/real

Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization

https://github.com/yaoliucs/PQL

Provably Good Batch Reinforcement Learning Without Great Exploration

https://github.com/yaoyao-liu/mini-imagenet-tools

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

https://github.com/yaringal/ConcreteDropout

Concrete Dropout

https://github.com/yaringal/HeteroscedasticDropoutUncertainty

Deep Exploration via Bootstrapped DQN

https://github.com/yashchandak/SAS_RL

Reinforcement Learning When All Actions are Not Always Available

https://github.com/yashkant/PNAS-Binarized-Neural-Networks

Progressive Neural Architecture Search

https://github.com/yashpatel5400/clean_idp_rl

TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

https://github.com/yaxinchen666/dce_pricingRL

Playing Atari with Deep Reinforcement Learning

https://github.com/yhisaki/average-reward-drl

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

https://github.com/yifan123/arxiv_spider

False Correlation Reduction for Offline Reinforcement Learning

https://github.com/yifan12wu/td3-jax

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/yihaosun1124/OfflineRL-Kit

MOPO: Model-based Offline Policy Optimization

https://github.com/yihenglin97/networked-marl

Multi-Agent Reinforcement Learning in Stochastic Networked Systems

https://github.com/yiliu1/Machine-Learning-in-Finance

Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

https://github.com/yimingpeng/sac-master

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

https://github.com/yining043/SAC-discrete

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

https://github.com/yiqinyang/vem

Offline Reinforcement Learning with Value-based Episodic Memory

https://github.com/yiwang89/fmfn-BayesianOptimization

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/yiwc/robotics-world

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/yiyuezhuo/A-Tutorial-on-Bayesian-Optimization-exmaple

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

https://github.com/yizhangliu/android_env_for_windows

AndroidEnv: A Reinforcement Learning Platform for Android

https://github.com/yl3829/Q-map

Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

https://github.com/yliess86/Bayeformers

Weight Uncertainty in Neural Networks

https://github.com/ylqi/gl-rg

Self-critical Sequence Training for Image Captioning

https://github.com/yly-revive/chainer-mreader

Reinforced Mnemonic Reader for Machine Reading Comprehension

https://github.com/ymlwww/rlproject

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

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