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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/g0k0ururi/crop

공식 코드

CROP: Conservative Reward for Model-based Offline Policy Optimization

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

공식 코드

Rethinking Attention with Performers

https://github.com/google/trax/tree/master/trax/models/reformer

공식 코드

Reformer: The Efficient Transformer

https://github.com/hanjuku-kaso/awesome-offline-rl

공식 코드

CORL: Research-oriented Deep Offline Reinforcement Learning Library

https://github.com/hmomin/networkgym

공식 코드

NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation

https://github.com/huawei-noah/HEBO/tree/master/PMDB

공식 코드

Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief

https://github.com/huawei-noah/hebo

공식 코드

Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief

https://github.com/huey0528/icml24didi

공식 코드

DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation

https://github.com/idiap/fast-transformers

공식 코드

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

https://github.com/ikostrikov/implicit_q_learning

공식 코드

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/imoneoi/RSP_JAX

공식 코드

Are Expressive Models Truly Necessary for Offline RL?

https://github.com/jihwan-jeong/cbop

공식 코드

Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization

https://github.com/kzl/decision-transformer

공식 코드

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/lastTarnished/Hypyercud-Policy-Regularization

공식 코드

Hypercube Policy Regularization Framework for Offline Reinforcement Learning

https://github.com/lasttarnished/gpc-sac

공식 코드

Grid-Mapping Pseudo-Count Constraint for Offline Reinforcement Learning

https://github.com/ldcq/ldcq

공식 코드

Reasoning with Latent Diffusion in Offline Reinforcement Learning

https://github.com/leaplabthu/famo2o

공식 코드

Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning

https://github.com/leaveslei/obd

공식 코드

Offline Behavior Distillation

https://github.com/liuzuxin/dsrl

공식 코드

Datasets and Benchmarks for Offline Safe Reinforcement Learning

https://github.com/liuzuxin/fsrl

공식 코드

Datasets and Benchmarks for Offline Safe Reinforcement Learning

https://github.com/liuzuxin/osrl

공식 코드

Datasets and Benchmarks for Offline Safe Reinforcement Learning

https://github.com/ltlhuuu/PSEC

공식 코드

Skill Expansion and Composition in Parameter Space

https://github.com/ltlhuuu/a2pr

공식 코드

Adaptive Advantage-Guided Policy Regularization for Offline Reinforcement Learning

https://github.com/lucascjysdl/offline-rl-kit

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Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

https://github.com/marvinalles/c-lap

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Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning

https://github.com/meowatthemoon/decisionmamba

공식 코드

Decision Mamba Architectures

https://github.com/meowatthemoon/hierarchicaldecisionmamba

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Decision Mamba Architectures

https://github.com/mhngu23/beyond-the-known-decision-making-with-counterfactual1-reasoning-decision-transformer

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Beyond the Known: Decision Making with Counterfactual Reasoning Decision Transformer

https://github.com/microsoft/atac

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Adversarially Trained Actor Critic for Offline Reinforcement Learning

https://github.com/mohammadrezanakhaei/relce

공식 코드

Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning

https://github.com/motokiomura/MXQL

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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/pipixiaqishi1/tatu

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

https://github.com/plankson/a2po

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

https://github.com/plankson/csirl

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Curricular Subgoals for Inverse Reinforcement Learning

https://github.com/prajjwal1/rl_paradigm

공식 코드

When should we prefer Decision Transformers for Offline Reinforcement Learning?

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