Papers with Code 아카이브

Offline RL

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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/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/nakamotoo/v-gps

Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance

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

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/opendilab/DI-engine/blob/main/ding/policy/td3_bc.py

A Minimalist Approach to Offline Reinforcement Learning

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

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/pcchenxi/lapo-offlienrl

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL

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

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/phixion/phixion

Reinforcement Learning: An Overview

https://github.com/polixir/OfflineRL

MOPO: Model-based Offline Policy Optimization

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/cql

Conservative Q-Learning for Offline Reinforcement Learning

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/ray-project/ray/tree/master/rllib

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/rlacombe/adsorbrl

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/rmrafailov/lompo

Offline Reinforcement Learning from Images with Latent Space Models

https://github.com/scottemmons/youngs-cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/sea-snell/implicit-language-q-learning

Offline RL for Natural Language Generation with Implicit Language Q Learning

https://github.com/secury/optidice

OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation

https://github.com/seohongpark/ogbench

Is Value Learning Really the Main Bottleneck in Offline RL?

https://github.com/shyamal-anadkat/offlinerl

d3rlpy: An Offline Deep Reinforcement Learning Library

https://github.com/skandermoalla/bet-reproduction

Behavior Transformers: Cloning $k$ modes with one stone

https://github.com/sliao-mi-luku/Chatbot-Reformer

Reformer: The Efficient Transformer

https://github.com/sliao-mi-luku/NLP-Chatbot-Reformer-Trax

Reformer: The Efficient Transformer

https://github.com/snu-mllab/edac

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble

https://github.com/sparkmxy/my-offlinerl

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/stanford-iris-lab/batch-exploration

Batch Exploration with Examples for Scalable Robotic Reinforcement Learning

https://github.com/teddykoker/performer

Rethinking Attention with Performers

https://github.com/tensorflow/models/tree/master/official/nlp/modeling

Rethinking Attention with Performers

https://github.com/thibaultdouzon/long-range-document-transformer

cosFormer: Rethinking Softmax in Attention

https://github.com/tinkoff-ai/lb-sac

Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size

https://github.com/typoverflow/OfflineRL-Lib

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/vub-ai-lab/qwr

Q-Value Weighted Regression: Reinforcement Learning with Limited Data

https://github.com/wenzhe-li/romi

Offline Reinforcement Learning with Reverse Model-based Imagination

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