Papers with Code 아카이브

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/karishnu/tf-agents-multi-particle-envs

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

https://github.com/karush17/emix

Energy-based Surprise Minimization for Multi-Agent Value Factorization

https://github.com/kathyrnrouse/RL_CUIP

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks

https://github.com/kdally/fault-tolerant-flight-control-drl

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

https://github.com/keep9oing/DRQN-Pytorch-CartPole-v1

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/kevin5naug/summer_project

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/kevslinger/dtqn

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/kishorkuttan/Multi-Agent-Deep-Deterministic-Policy-Gradient-actor-critic-deep-reinforcement-for-capacity-manageme

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

https://github.com/kmdanielduan/DQN_Family_PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/kochlisGit/autonomous-vehicles-agent

Deep Reinforcement Learning with Double Q-learning

https://github.com/komejisatori/ReinforcementCar

Playing Atari with Deep Reinforcement Learning

https://github.com/kondrasso/DQN

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

https://github.com/krasing/DRLearningCollaboration

Continuous control with deep reinforcement learning

https://github.com/krasing/DRLearningContinuousControl

Continuous control with deep reinforcement learning

https://github.com/ksajan/DDPG-MAPE

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

https://github.com/kshitij-ingale/Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ktkachuk/Atari-with-Q-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ku2482/discor.pytorch

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

https://github.com/ku2482/gail-airl-ppo.pytorch

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

https://github.com/ku2482/rljax

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

https://github.com/ku2482/rltorch

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

https://github.com/ku2482/soft-actor-critic.pytorch

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

https://github.com/kushagra06/DDPG

Continuous control with deep reinforcement learning

https://github.com/kushagra06/SAC

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

https://github.com/l4fl4m3/bmg

Bootstrapped Meta-Learning

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Playing Atari with Deep Reinforcement Learning

https://github.com/lachisis/multiagent-particle-envs

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

https://github.com/lanqingli1993/focal-iclr

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

https://github.com/lbenbaccar/Deep-Reinforcement-Learning-with-Double-Q-Learning

Deep Reinforcement Learning with Double Q-learning

https://github.com/learn-to-race/l2r

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

https://github.com/lexfridman/deeptraffic

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/liampetti/DDPG

Continuous control with deep reinforcement learning

https://github.com/linhlpv/Offline-and-OfflineOnline-RL

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/liuyuezhang/pyrl

Continuous control with deep reinforcement learning

https://github.com/lollcat/Soft-Actor-Critic

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

https://github.com/lucadellalib/sac-beta

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

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