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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/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/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/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/Jaroan/Fair-MARL

Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

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

https://github.com/JohanSamir/rainbow_extend

Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

https://github.com/JohannesAck/tf2multiagentrl

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

https://github.com/John-Yu/leelaApplication

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

https://github.com/JohnYKiyo/coco_trial

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

https://github.com/JonasRSV/DDPG

Continuous control with deep reinforcement learning

https://github.com/JonasRSV/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/JonasRSV/DQNTensorflow

Playing Atari with Deep Reinforcement Learning

https://github.com/JonasRSV/PGTensorflow

Continuous control with deep reinforcement learning

https://github.com/JorgeA-RD/Reinforcement-Learning-Agents/tree/main/DDPG

Continuous control with deep reinforcement learning

https://github.com/JoseVillagranE/Pointer-Networks

Neural Combinatorial Optimization with Reinforcement Learning

https://github.com/JulT1/RL_SS19

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/JuliaPOMDP/DeepQLearning.jl

Playing Atari with Deep Reinforcement Learning

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