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Q-Learning

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The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

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

https://github.com/xiuyu0000/new_papers_codes/tree/main/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/xiuyu0000/new_papers_codes/tree/main/sac

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

https://github.com/xtma/simple-pytorch-rl

Continuous control with deep reinforcement learning

https://github.com/xuehy/pytorch-maddpg

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

https://github.com/xukaidi13/Simulation-code

Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet

https://github.com/xusophia/DataSciFinalProj

Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning

https://github.com/xuyuandong/simple-ddpg

Continuous control with deep reinforcement learning

https://github.com/xuzijian629/combopt-zero

Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero

https://github.com/xyshadow/baseline_ddpg

Continuous control with deep reinforcement learning

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

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

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/yifan12wu/td3-jax

Addressing Function Approximation Error in Actor-Critic Methods

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

Conservative Q-Learning for Offline Reinforcement Learning

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/yl3829/Q-map

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

https://github.com/young-geng/cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/young-geng/jaxcql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/yuchaoch/project/tree/main/Collaborative_Multi_BS_Power_Management_for_Dense_Radio_Access_Network_using_Deep_Reinforcement_Learning

Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning

https://github.com/yukezhu/tensorflow-reinforce

Continuous control with deep reinforcement learning

https://github.com/yusme/DDPG

Continuous control with deep reinforcement learning

https://github.com/yydsok/oparl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/yzheng51/rl-dino-run

Deep Reinforcement Learning with Double Q-learning

https://github.com/zhengant/dqn_reversi

Deep Reinforcement Learning with Double Q-learning

https://github.com/zoeyuchao/MPE-pytorch

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

https://github.com/zoeyuchao/MPEnew-pytorch

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

https://github.com/zowiezhang/stas

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

https://github.com/zzmtsvv/ORL

Mildly Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/zzmtsvv/rl_task

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

https://gitlab.com/stchrist/ViscousThreads

Remember and Forget for Experience Replay

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