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코드

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

https://github.com/motokiomura/MXQL

공식 코드

Stabilizing Extreme Q-learning by Maclaurin Expansion

https://github.com/oh-lab/count-morl

공식 코드

Model-based Offline Reinforcement Learning with Count-based Conservatism

https://github.com/paperwithcode/pop3d

공식 코드

Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization

https://github.com/pipixiaqishi1/tatu

공식 코드

Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning

https://github.com/plankson/a2po

공식 코드

A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective

https://github.com/plankson/csirl

공식 코드

Curricular Subgoals for Inverse Reinforcement Learning

https://github.com/prajjwal1/rl_paradigm

공식 코드

When should we prefer Decision Transformers for Offline Reinforcement Learning?

https://github.com/purewater0901/scq

공식 코드

Strategically Conservative Q-Learning

https://github.com/qinwenluo/ssar

공식 코드

Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL

https://github.com/rail-berkeley/d4rl

공식 코드

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/rail-berkeley/offline_rl

공식 코드

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

https://github.com/rail-berkeley/rlkit

공식 코드

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/renming-huang/trajectorydiffuser

공식 코드

Diffusion Models as Optimizers for Efficient Planning in Offline RL

https://github.com/robfiras/ls-iq

공식 코드

LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning

https://github.com/runamu/compositional-conservatism

공식 코드

Compositional Conservatism: A Transductive Approach in Offline Reinforcement Learning

https://github.com/ruoqizzz/entropy-offlineRL

공식 코드

Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning

https://github.com/ryanxhr/ivr

공식 코드

Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization

https://github.com/ryanxhr/por

공식 코드

A Policy-Guided Imitation Approach for Offline Reinforcement Learning

https://github.com/sail-sg/edp

공식 코드

Efficient Diffusion Policies for Offline Reinforcement Learning

https://github.com/sail-sg/misa

공식 코드

Mutual Information Regularized Offline Reinforcement Learning

https://github.com/sail-sg/offbench

공식 코드

Improving and Benchmarking Offline Reinforcement Learning Algorithms

https://github.com/seohongpark/fql

공식 코드

Flow Q-Learning

https://github.com/sharkwyf/cgdt

공식 코드

Critic-Guided Decision Transformer for Offline Reinforcement Learning

https://github.com/silihuang-ai/idt

공식 코드

In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought

https://github.com/takuseno/d3rlpy

공식 코드

d3rlpy: An Offline Deep Reinforcement Learning Library

https://github.com/thu-ml/cep-energy-guided-diffusion

공식 코드

Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning

https://github.com/thu-ml/efficient-diffusion-alignment

공식 코드

Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control

https://github.com/thu-ml/srpo

공식 코드

Score Regularized Policy Optimization through Diffusion Behavior

https://github.com/thuml/Flowformer

공식 코드

Flowformer: Linearizing Transformers with Conservation Flows

https://github.com/tianyucodings/diffusion_trusted_q_learning

공식 코드

Diffusion Policies creating a Trust Region for Offline Reinforcement Learning

https://github.com/tinkoff-ai/CORL

공식 코드

CORL: Research-oriented Deep Offline Reinforcement Learning Library

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

공식 코드

Anti-Exploration by Random Network Distillation

https://github.com/tsutikgiau/valuememorygraph

공식 코드

Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning

https://github.com/victor-wang-902/synthetic-pretrain-rl

공식 코드

Pre-training with Synthetic Data Helps Offline Reinforcement Learning

https://github.com/wkh923/m3pc

공식 코드

M$^3$PC: Test-time Model Predictive Control for Pretrained Masked Trajectory Model

https://github.com/yingyichen-cyy/PrimalAttention

공식 코드

Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation

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