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D4RL

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코드

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

https://github.com/BY571/Implicit-Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/Delavari-Alireza/Decision_Transformer_Sequence-Modeling_Deep_Reinforcement_Learning

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/nn/attention.py

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/HzcIrving/DecisionTransformer_StepbyStep

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/LAS1520/Gato-A-Generalist-Agent

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Manchery/iql-pytorch

Offline Reinforcement Learning with Implicit Q-Learning

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

Reformer: The Efficient Transformer

https://github.com/MohammadrezaNakhaei/FQL

Flow Q-Learning

https://github.com/RamiSketcher/decision-transformer-ammi

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/Rick-McCoy/Reformer-pytorch

Reformer: The Efficient Transformer

https://github.com/adamjelley/efficientofflinerl

Revisiting the Minimalist Approach to Offline Reinforcement Learning

https://github.com/ankurhanda/implicit-bc-2d

Implicit Behavioral Cloning

https://github.com/anuragajay/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/cannylab/casual_overhypotheses

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/charleshsc/HarmoDT

Q-value Regularized Transformer for Offline Reinforcement Learning

https://github.com/charleshsc/qt

Q-value Regularized Transformer for Offline Reinforcement Learning

https://github.com/chinganc/lightatac

Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/daniellawson9999/online-decision-transformer

Online Decision Transformer

https://github.com/dasgringuen/assetto_corsa_gym

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/davidsvy/cosformer-pytorch

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/div99/xql

Extreme Q-Learning: MaxEnt RL without Entropy

https://github.com/facebookresearch/salina

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/facebookresearch/xformers

Rethinking Attention with Performers

https://github.com/farama-foundation/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/glassroom/heinsen_attention

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/google-research/ibc

Implicit Behavioral Cloning

https://github.com/gwthomas/iql-pytorch

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/hmomin/CORL-compare

CORL: Research-oriented Deep Offline Reinforcement Learning Library

https://github.com/howuhh/sac-n-jax

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble

https://github.com/huggingface/transformers

Reformer: The Efficient Transformer

https://github.com/junnyu/paddle_reformer

Reformer: The Efficient Transformer

https://github.com/kevinzakka/ibc

Implicit Behavioral Cloning

https://github.com/koulanurag/opcc

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/kpertsch/d4rl

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/kyle-gao/TF_Transformer

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

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