Papers with Code 아카이브

Offline RL

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하위 태스크

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평가표

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보존된 평가 기록

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코드

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

https://github.com/enpasos/muzero

Online and Offline Reinforcement Learning by Planning with a Learned Model

https://github.com/eric-mitchell/macaw-min

Offline Meta-Reinforcement Learning with Advantage Weighting

https://github.com/facebookresearch/Horizon

Critic Regularized Regression

https://github.com/facebookresearch/ReAgent

Critic Regularized Regression

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/gaetanserre/lipo

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/glassroom/heinsen_attention

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

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

Acme: A Research Framework for Distributed Reinforcement Learning

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

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/haosulab/ManiSkill-Learn

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/howuhh/faster-trajectory-transformer

Offline Reinforcement Learning as One Big Sequence Modeling Problem

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/ikostrikov/cql-results

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/ikostrikov/rlpd

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

https://github.com/jhejna/cpl

Direct Preference-based Policy Optimization without Reward Modeling

https://github.com/junming-yang/mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/junnyu/paddle_reformer

Reformer: The Efficient Transformer

https://github.com/karim-abdel/fact

Explaining RL Decisions with Trajectories

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/kschweig/OfflineRL

A Dataset Perspective on Offline 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/manantomar/video-occupancy-models

Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information

https://github.com/maohangyu/TIT_open_source

PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning

https://github.com/mehdimashayekhi/Some-RL-Implementation

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/microsoft/lightatac

Adversarially Trained Actor Critic for Offline Reinforcement Learning

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