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

https://github.com/jonaths/dqn-grid

Playing Atari with Deep Reinforcement Learning

https://github.com/jonaths/tf-dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/jongharyu/neural-svd

Spectral Inference Networks: Unifying Deep and Spectral Learning

https://github.com/jongornet14/HyperController

Proximal Policy Optimization Algorithms

https://github.com/joshiatul/game_playing

Playing Atari with Deep Reinforcement Learning

https://github.com/jrobine/actor-critic

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/jsztompka/DuelDQN

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/jsztompka/MultiAgent-PPO

Proximal Policy Optimization Algorithms

https://github.com/jsztompka/PPO-demo

Proximal Policy Optimization Algorithms

https://github.com/jugg1er/air

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/juliusfrost/Research-Paper-Implementations

Proximal Policy Optimization Algorithms

https://github.com/justinmaojones/starr

Prioritized Experience Replay

https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution

Proximal Policy Optimization Algorithms

https://github.com/jvoynow/DQN-analysis-with-2048

Deep Reinforcement Learning with Double Q-learning

https://github.com/jw1401/PPO-Tensorflow-2.0

Proximal Policy Optimization Algorithms

https://github.com/k-lombard/CS4641_Project

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/k-lombard/Deep-Learning-Chess-AI

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/kachayev/gym-microrts-paper-sb3

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/kaesve/muzero

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

https://github.com/kaichiuwong/rlhps

Deep reinforcement learning from human preferences

https://github.com/kamsyn95/CL_DNN

Overcoming catastrophic forgetting in neural networks

https://github.com/karush17/emix

Energy-based Surprise Minimization for Multi-Agent Value Factorization

https://github.com/kavosh8/MAC

Mean Actor Critic

https://github.com/kayuksel/pytorch-ars

Prioritized Experience Replay

https://github.com/kcorder/qmix_variants

The StarCraft Multi-Agent Challenge

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

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/keraJLi/rejax

Soft Actor-Critic for Discrete Action Settings

https://github.com/kered9/gym-microrts

Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games

https://github.com/kevslinger/dtqn

Deep Recurrent Q-Learning for Partially Observable MDPs

https://github.com/kfu02/jaxmarl

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/khanhptnk/bandit-nmt

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/kinalmehta/epymarl

The StarCraft Multi-Agent Challenge

https://github.com/kmdanielduan/DQN_Family_PyTorch

Playing Atari with Deep Reinforcement Learning

https://github.com/kngwyu/intrinsic-rewards

Exploration by Random Network Distillation

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

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