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코드

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

https://github.com/2023annonymousauthor/csve

공식 코드

Conservative State Value Estimation for Offline Reinforcement Learning

https://github.com/BayesBrain/Habi

공식 코드

Habitizing Diffusion Planning for Efficient and Effective Decision Making

https://github.com/Div99/IQ-Learn

공식 코드

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/Facebear-ljx/DOGE

공식 코드

When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning

https://github.com/Fang-Lin93/DAC

공식 코드

Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning

https://github.com/Improbable-AI/dw-offline-rl

공식 코드

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets

https://github.com/JasonMa2016/CODAC

공식 코드

Conservative Offline Distributional Reinforcement Learning

https://github.com/Kaixhin/imitation-learning

공식 코드

A Pragmatic Look at Deep Imitation Learning

https://github.com/MaxSobolMark/OOO

공식 코드

Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias

https://github.com/MiaHRT/CAWR

공식 코드

CAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization

https://github.com/MianchuWang/LOM

공식 코드

Learning on One Mode: Addressing Multi-Modality in Offline Reinforcement Learning

https://github.com/OpenNLPLab/cosFormer

공식 코드

cosFormer: Rethinking Softmax in Attention

https://github.com/PrajwalKoirala/Offline-Reinforcement-Learning-with-Decision-Tree-Regression

공식 코드

Solving Offline Reinforcement Learning with Decision Tree Regression

https://github.com/QingyuanWuNothing/DFBT

공식 코드

Directly Forecasting Belief for Reinforcement Learning with Delays

https://github.com/baichenjia/pbrl

공식 코드

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

https://github.com/changchencc/plandq

공식 코드

PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer

https://github.com/chendrag/sfbc

공식 코드

Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling

https://github.com/cloud0723/offline-mlirl

공식 코드

When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

https://github.com/corl-team/CORL

공식 코드

CORL: Research-oriented Deep Offline Reinforcement Learning Library

https://github.com/corl-team/katakomba

공식 코드

Katakomba: Tools and Benchmarks for Data-Driven NetHack

https://github.com/cxxgtxy/POP3D

공식 코드

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

https://github.com/czp16/cde-offline-rl

공식 코드

Learning from Sparse Offline Datasets via Conservative Density Estimation

https://github.com/davidbrandfonbrener/onestep-rl

공식 코드

Offline RL Without Off-Policy Evaluation

https://github.com/davidbrandfonbrener/rcsl-paper

공식 코드

When does return-conditioned supervised learning work for offline reinforcement learning?

https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged

공식 코드

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

https://github.com/deepmind/open_spiel

공식 코드

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

https://github.com/dmksjfl/CABI

공식 코드

Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination

https://github.com/dmksjfl/mcq

공식 코드

Mildly Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/dmksjfl/seabo

공식 코드

SEABO: A Simple Search-Based Method for Offline Imitation Learning

https://github.com/dragon-zhuang/bppo

공식 코드

Behavior Proximal Policy Optimization

https://github.com/dragon-zhuang/reinformer

공식 코드

Reinformer: Max-Return Sequence Modeling for Offline RL

https://github.com/dt6a/actoreg

공식 코드

The Role of Deep Learning Regularizations on Actors in Offline RL

https://github.com/dt6a/rebrac

공식 코드

Revisiting the Minimalist Approach to Offline Reinforcement Learning

https://github.com/egg-west/mcep

공식 코드

Mildly Constrained Evaluation Policy for Offline Reinforcement Learning

https://github.com/ermongroup/MetaIRL

공식 코드

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

https://github.com/ethanluoyc/optimal_transport_reward

공식 코드

Optimal Transport for Offline Imitation Learning

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