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/facebookresearch/benchmarl

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

https://github.com/facebookresearch/rl/blob/main/examples/ddpg/ddpg.py

Continuous control with deep reinforcement learning

https://github.com/facebookresearch/rl/blob/main/examples/dqn/dqn.py

Playing Atari with Deep Reinforcement Learning

https://github.com/facebookresearch/rl/blob/main/examples/sac/sac.py

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

https://github.com/facebookresearch/rl/tree/main/examples/redq/redq.py

Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

https://github.com/fapont/hackaton-hiparis-2021

Momentum-based Accelerated Q-learning

https://github.com/farama-foundation/vizdoom

ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

https://github.com/fdcl-gwu/gym-rotor

Continuous control with deep reinforcement learning

https://github.com/fengsterooni/dql

Deep Reinforcement Learning with Double Q-learning

https://github.com/feruxhi/thoughts

Continuous control with deep reinforcement learning

https://github.com/fhbzc/FishAgentSimulation

Continuous control with deep reinforcement learning

https://github.com/fiberleif/evolution-strategies

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/fiberleif/nc_ddpg

Continuous control with deep reinforcement learning

https://github.com/filippogiruzzi/deep_q_learning

Playing Atari with Deep Reinforcement Learning

https://github.com/fiorenza2/OffCon3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/flavioschneider/ml_papers_presentations

Continuous control with deep reinforcement learning

https://github.com/floodsung/DDPG

Continuous control with deep reinforcement learning

https://github.com/flowersteam/curious

Continuous control with deep reinforcement learning

https://github.com/flowersteam/rl_stats

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

https://github.com/fmorenovr/Attention_Mechanism

Performing Deep Recurrent Double Q-Learning for Atari Games

https://github.com/fshamshirdar/pytorch-rdpg

Continuous control with deep reinforcement learning

https://github.com/gaetanserre/lipo

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/garyzyr001/rethinking-airl

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

https://github.com/gcomb/GCOMB

Learning Heuristics over Large Graphs via Deep Reinforcement Learning

https://github.com/geeky-wizard/Atari-Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/georgesung/TD3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/georgkruse/cleanqrl

Continuous control with deep reinforcement learning

https://github.com/ghliu/pytorch-ddpg

Continuous control with deep reinforcement learning

https://github.com/gijskoning/ReproducingCURL

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

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

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

https://github.com/goldbattle/snakes_mal

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

https://github.com/gomerudo/nas-dmrl

BlockQNN: Efficient Block-wise Neural Network Architecture Generation

https://github.com/gomerudo/nas-env

BlockQNN: Efficient Block-wise Neural Network Architecture Generation

https://github.com/google-deepmind/csil

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/google/maddpg-replication

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

https://github.com/gordicaleksa/pytorch-learn-reinforcement-learning

Playing Atari with Deep Reinforcement Learning

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