https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/nn/attention.py
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Papers with Code 아카이브
General Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HoldenB/TD3-BC-from-scratch
https://github.com/HumanCompatibleAI/deep-rlsp
https://github.com/HzcIrving/DecisionTransformer_StepbyStep
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/JianGuanTHU/RecGAN
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
https://github.com/John-Yu/leelaApplication
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/JunnYu/x-transformers-paddle
https://github.com/LAS1520/Gato-A-Generalist-Agent
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/Learning-and-Intelligent-Systems/predicators_behavior
Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
https://github.com/LucasAlegre/sac-plus
https://github.com/MVerghese/NN-Dynamics-Sparse-Reward
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/Manchery/iql-pytorch
https://github.com/MerceaOtniel/HybridAlpha
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/MindCode-4/code-2/tree/main/qdqbert
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
https://github.com/MindCode-4/code-3/tree/main/decision_transformer
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/MindCode-4/code-3/tree/main/efficientformer
https://github.com/MindCode-4/code-4/tree/main/qdqbert
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
https://github.com/MindCode-4/code-8/tree/main/Neural-Network-based-Speech-Enhancement
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/MindSpore-scientific-2/code-5/tree/main/Integrated-Gradient
https://github.com/MingzhenY/Projects
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/MoMe36/BranchingDQN
Action Branching Architectures for Deep Reinforcement Learning
https://github.com/MohammadrezaNakhaei/FQL
https://github.com/Neo-The1/ThinkingTicTacToe
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/Nkluge-correa/teeny-tiny_castle
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/Olloxan/Pytorch-A2C
Imagination-Augmented Agents for Deep Reinforcement Learning
https://github.com/Qtsho/model-based-rl-navigation
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
https://github.com/QueensGambit/CrazyAra
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/QueensGambit/CrazyAra-Engine
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/RamiSketcher/decision-transformer-ammi
Decision Transformer: Reinforcement Learning via Sequence Modeling
https://github.com/Rick-McCoy/Reformer-pytorch
https://github.com/Shunichi09/PythonLinearNonlinearControl
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/Sidus-smg/ataxx-ai
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
https://github.com/StanfordVL/GibsonSim2RealChallenge
https://github.com/SwapnilPande/MOReL
https://github.com/Tyjch/open_spiel
https://github.com/Tyjch/open_spiel_colab
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.