Papers with Code 아카이브

Offline RL

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

2

보존된 평가 기록

4

코드

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

https://github.com/philippe-eecs/idql

공식 코드

IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

https://github.com/pipixiaqishi1/tatu

공식 코드

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

https://github.com/pku-marl/dexteroushands

공식 코드

Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

https://github.com/polixir/NeoRL

공식 코드

NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

https://github.com/polixir/neorl2

공식 코드

NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios

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/qianlin04/prmorl

공식 코드

Policy-regularized Offline Multi-objective Reinforcement Learning

https://github.com/qinwenluo/ssar

공식 코드

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

https://github.com/qiwang067/CoWorld

공식 코드

Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning

https://github.com/quantumiracle/consistency_model_for_reinforcement_learning

공식 코드

Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning

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/reinholdm/offline-pre-trained-multi-agent-decision-transformer

공식 코드

Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks

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

공식 코드

Diffusion Models as Optimizers for Efficient Planning in Offline RL

https://github.com/rlatjddbs/ssd

공식 코드

Stitching Sub-Trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL

https://github.com/roythuly/obac

공식 코드

Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL

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/ryanshea10/personachat_offline_rl

공식 코드

Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning

https://github.com/ryanxhr/dwbc

공식 코드

Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations

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/sail-sg/oper

공식 코드

Decoupled Prioritized Resampling for Offline RL

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

공식 코드

Efficient Offline Policy Optimization with a Learned Model

https://github.com/samholt/neurallaplacecontrol

공식 코드

Neural Laplace Control for Continuous-time Delayed Systems

https://github.com/scottemmons/rvs

공식 코드

RvS: What is Essential for Offline RL via Supervised Learning?

https://github.com/securitygames/oef

공식 코드

Offline Equilibrium Finding

https://github.com/seohongpark/fql

공식 코드

Flow Q-Learning

https://github.com/sfujim/TD3_BC

공식 코드

A Minimalist Approach to Offline Reinforcement Learning

https://github.com/sharkwyf/cgdt

공식 코드

Critic-Guided Decision Transformer for Offline Reinforcement Learning

https://github.com/shentao-yang/sdm-gan_icml2022

공식 코드

Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

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