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Offline RL

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

https://github.com/ldcq/ldcq

공식 코드

Reasoning with Latent Diffusion in Offline Reinforcement Learning

https://github.com/lemutisme/dr-sac

공식 코드

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty

https://github.com/lifelong-ml/offline-compositional-rl-datasets

공식 코드

Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning

https://github.com/lingweizhu/fat2thin

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Fat-to-Thin Policy Optimization: Offline RL with Sparse Policies

https://github.com/link-zju/orl-auditor

공식 코드

ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

https://github.com/lionelblonde/giwr-pytorch

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Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning

https://github.com/lucascjysdl/dgms-for-offline-policy-learning

공식 코드

Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions

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

공식 코드

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

https://github.com/machelreid/can-wikipedia-help-offline-rl

공식 코드

Can Wikipedia Help Offline Reinforcement Learning?

https://github.com/mamba413/room

공식 코드

Robust Offline Reinforcement learning with Heavy-Tailed Rewards

https://github.com/manantomar/agent-centric-representations

공식 코드

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

https://github.com/maohangyu/pdit

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

https://github.com/maoliyuan/odice-pytorch

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ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient Update

https://github.com/maoyixiu/dmg

공식 코드

Doubly Mild Generalization for Offline Reinforcement Learning

https://github.com/marc-rigter/1r2r

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One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning

https://github.com/marc-rigter/rambo

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RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning

https://github.com/martius-lab/ppgs

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Planning from Pixels in Environments with Combinatorially Hard Search Spaces

https://github.com/martius-lab/puzzlegen

공식 코드

Planning from Pixels in Environments with Combinatorially Hard Search Spaces

https://github.com/mary-wu/smdp

공식 코드

Semi-Markov Offline Reinforcement Learning for Healthcare

https://github.com/matsuolab/BREMEN

공식 코드

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

https://github.com/maximecb/gym-miniworld

공식 코드

DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs

https://github.com/microsoft/atac

공식 코드

Adversarially Trained Actor Critic for Offline Reinforcement Learning

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

공식 코드

A Dataset Perspective on Offline Reinforcement Learning

https://github.com/mld3/offlinerl_factoredactions

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Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

https://github.com/moreanp/osc

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Policy Constraint by Only Support Constraint for Offline Reinforcement Learning

https://github.com/motokiomura/MXQL

공식 코드

Stabilizing Extreme Q-learning by Maclaurin Expansion

https://github.com/nakamotoo/cal-ql

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Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

https://github.com/natashamjaques/neural_chat

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Human-centric Dialog Training via Offline Reinforcement Learning

https://github.com/nicoladainese96/code-world-models

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Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search

https://github.com/notmahi/bet

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Behavior Transformers: Cloning $k$ modes with one stone

https://github.com/nusnlp/prs

공식 코드

Preference-Guided Reflective Sampling for Aligning Language Models

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

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Model-based Offline Reinforcement Learning with Count-based Conservatism

https://github.com/openai/procgen

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Differentiable Tree Search Network

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

공식 코드

A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning

https://github.com/opendilab/GenerativeRL

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Revisiting Generative Policies: A Simpler Reinforcement Learning Algorithmic Perspective

https://github.com/pcheng2/tsrl

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Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL

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