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/SAGNIKMJR/MetaQNN_ImageClassification_PyTorch

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/SAGNIKMJR/MetaQNN_ImageGenerationGAN_DiscriminatorFixed_PyTorch

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/SAGNIKMJR/MetaQNN_ImageGenerationVCAE_PyTorch

Designing Neural Network Architectures using Reinforcement Learning

https://github.com/SVJayanthi/dmcontrol-generalization-benchmark

Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation

https://github.com/SaminYeasar/off_policy_ac

Continuous control with deep reinforcement learning

https://github.com/SayhoKim/tetrisRL

Playing Atari with Deep Reinforcement Learning

https://github.com/SergiPonsa/Reinforcement-Learning-Sergi

Continuous control with deep reinforcement learning

https://github.com/SeungeonBaek/continuous-agents-test

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ShangtongZhang/DistributedES

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/ShawK91/Evolutionary-Reinforcement-Learning

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

https://github.com/ShawK91/erl_paper_nips18

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

https://github.com/Sheepsody/Batched-Impala-PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/Sheldonmao/Learning-To-walk

Continuous control with deep reinforcement learning

https://github.com/ShivamShrirao/deep_Q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/ShivamShrirao/deep_Q_learning_from_scratch

Playing Atari with Deep Reinforcement Learning

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

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

https://github.com/SimonRamstedt/ddpg

Continuous control with deep reinforcement learning

https://github.com/SintolRTOS/multi-agent_Example

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

https://github.com/Souphis/mobile_robot_rl

Continuous control with deep reinforcement learning

https://github.com/Steinheilig/Imbiss

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

https://github.com/Steven-Ho/multiagent-particle-envs

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

https://github.com/Stippler/cow-simulator

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

https://github.com/SwarajPawar/Discrete-BCQ

Benchmarking Batch Deep Reinforcement Learning Algorithms

https://github.com/T3chy/DDPG

Continuous control with deep reinforcement learning

https://github.com/TheFebrin/DeepRL-Pong

Playing Atari with Deep Reinforcement Learning

https://github.com/TheInfamousWayne/ddpg

Continuous control with deep reinforcement learning

https://github.com/TonghanWang/DOP

Multiagent Cooperation and Competition with Deep Reinforcement Learning

https://github.com/TonghanWang/NDQ

Multiagent Cooperation and Competition with Deep Reinforcement Learning

https://github.com/VasaKiDD/TD3-deep-rl-research

Continuous control with deep reinforcement learning

https://github.com/VictorZuanazzi/Project_RL

The Effects of Memory Replay in Reinforcement Learning

https://github.com/Wadaboa/flatland-challenge

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

https://github.com/Wentworth1996/Summer_Intern_Progress

Playing Atari with Deep Reinforcement Learning

https://github.com/WittmannF/quadcopter-best-practices

Continuous control with deep reinforcement learning

https://github.com/X3N4/car_racer

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

https://github.com/YangRui2015/Modular_HER

Continuous control with deep reinforcement learning

https://github.com/YoUNG824/DDPG

Continuous control with deep reinforcement learning

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