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코드

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

https://github.com/glassroom/heinsen_attention

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

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

IQ-Learn: Inverse soft-Q Learning for Imitation

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/huihanl/rl_unplugged

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

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/ku2482/gail-airl-ppo.pytorch

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

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

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/linhlpv/Offline-and-OfflineOnline-RL

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/lions-epfl/lion

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/lucashueda/long_sentence_transformer

Reformer: The Efficient Transformer

https://github.com/lucidrains/linear-attention-transformer

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/lucidrains/performer-pytorch

Rethinking Attention with Performers

https://github.com/lucidrains/reformer-pytorch

Reformer: The Efficient Transformer

https://github.com/microsoft/lightatac

Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/ml-jku/l2m

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/mpatacchiola/imujoco

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/mugoh/rl-base/tree/master/rlbase/aiRL

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/nikhilbarhate99/min-decision-transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/opendilab/DI-engine

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/orrivlin/implicit-q-learning

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/perrin-isir/afu

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/pwc-1/Paper-10/tree/main/transformer

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/pwc-1/Paper-8/tree/main/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/iql

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/quantumiracle/consistency_model_for_reinforcement_learning

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

https://github.com/ran-weii/cleanil

Implicit Behavioral Cloning

https://github.com/rohitrango/Reward-bias-in-GAIL

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/ryan-dorazio/mmd-dilated

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

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