Papers with Code 아카이브

General Reinforcement Learning

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하위 태스크: 2
평가표: 6
보존된 평가 기록: 12

코드

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

https://bitbucket.org/RLinRL/intelligenttrainerpublic

공식 코드

Intelligent Trainer for Model-Based Reinforcement Learning

https://github.com/011235813/lio

공식 코드

Learning to Incentivize Other Learning Agents

https://github.com/1jsingh/semantic-guidance

공식 코드

Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings

https://github.com/2019chengong/offline_rl_poisoner

공식 코드

BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets

https://github.com/2019chengong/trajdeleter

공식 코드

TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents

https://github.com/2m-kotb/curiosity-cem

공식 코드

Sample-efficient Real-time Planning with Curiosity Cross-Entropy Method and Contrastive Learning

https://github.com/Advanced-Research-Centre/QKSA

공식 코드

QKSA: Quantum Knowledge Seeking Agent

https://github.com/Altriaex/multi_source_behavior_modeling

공식 코드

Behavior Estimation from Multi-Source Data for Offline Reinforcement Learning

https://github.com/AndssY/DeMa

공식 코드

Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?

https://github.com/ArronDZhang/ROLeR

공식 코드

ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems

https://github.com/Bellman-devs/bellman

공식 코드

Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow

https://github.com/CLeARoboticsLab/ssrl

공식 코드

Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models

https://github.com/Data-Science-in-Mechanical-Engineering/macura

공식 코드

Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption

https://github.com/DavidLeeftink/probabilistic-pontryagin-control

공식 코드

Probabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning

https://github.com/EmptyJackson/groove

공식 코드

Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design

https://github.com/FOCAL-ICLR/FOCAL-ICLR

공식 코드

FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization

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

공식 코드

When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning

https://github.com/FilippoAiraldi/learning-safety-in-mpc-based-rl

공식 코드

Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes

https://github.com/GoodAI/torchsim

공식 코드

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

https://github.com/HengerLi/RLBackdoorFL

공식 코드

Learning to Backdoor Federated Learning

https://github.com/HewlettPackard/trust-ml

공식 코드

Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness

https://github.com/Holmeswww/ml-uwac

공식 코드

Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

https://github.com/HuTobias/HIGHLIGHTS-LRP

공식 코드

Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

https://github.com/HxLyn3/ADMPO

공식 코드

Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning

https://github.com/HxLyn3/MPPVE

공식 코드

Model-based Reinforcement Learning with Multi-step Plan Value Estimation

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

공식 코드

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets

https://github.com/JannerM/mbpo

공식 코드

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/JannerM/trajectory-transformer

공식 코드

Offline Reinforcement Learning as One Big Sequence Modeling Problem

https://github.com/JasonMa2016/CODAC

공식 코드

Conservative Offline Distributional Reinforcement Learning

https://github.com/JianGuanTHU/IRecGAN

공식 코드

Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation

https://github.com/LeCAR-Lab/CoVO-MPC

공식 코드

CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design

https://github.com/LiangZhang1996/DataLight

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DataLight: Offline Data-Driven Traffic Signal Control

https://github.com/Lukeeeeee/baconian-project

공식 코드

Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning

https://github.com/MAOYIXIU/SCAS

공식 코드

Offline Reinforcement Learning with OOD State Correction and OOD Action Suppression

https://github.com/MIRALab-USTC/RL-POMBU

공식 코드

Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization

https://github.com/MIRALab-USTC/RL-TRACER

공식 코드

Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions

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