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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://github.com/etalvitie/hdaggermc

공식 코드

Self-Correcting Models for Model-Based Reinforcement Learning

https://github.com/ethanluoyc/optimal_transport_reward

공식 코드

Optimal Transport for Offline Imitation Learning

https://github.com/evgenii-nikishin/omd

공식 코드

Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation

https://github.com/facebookresearch/drqv2

공식 코드

VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning

https://github.com/facebookresearch/gen_dgrl

공식 코드

The Generalization Gap in Offline Reinforcement Learning

https://github.com/facebookresearch/latent-space-priors

공식 코드

Leveraging Demonstrations with Latent Space Priors

https://github.com/facebookresearch/mbrl-lib

공식 코드

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

https://github.com/facebookresearch/modem

공식 코드

MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

https://github.com/facebookresearch/mtm

공식 코드

Masked Trajectory Models for Prediction, Representation, and Control

https://github.com/facebookresearch/nle

공식 코드

Dungeons and Data: A Large-Scale NetHack Dataset

https://github.com/facebookresearch/ssorl

공식 코드

Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories

https://github.com/facebookresearch/svg

공식 코드

On the model-based stochastic value gradient for continuous reinforcement learning

https://github.com/facebookresearch/vip

공식 코드

VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training

https://github.com/fangchenliu/maskdp_public

공식 코드

Masked Autoencoding for Scalable and Generalizable Decision Making

https://github.com/fdeng18/dreamer-pro

공식 코드

DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

https://github.com/felix-thu/AlignIQL

공식 코드

AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization

https://github.com/felix-thu/DiffCPS

공식 코드

DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning

https://github.com/filippoairaldi/mpcrl-cbf

공식 코드

Probabilistically safe and efficient model-based Reinforcement Learning

https://github.com/filippoairaldi/mpcrl-for-ramp-metering

공식 코드

Reinforcement Learning with Model Predictive Control for Highway Ramp Metering

https://github.com/g0k0ururi/crop

공식 코드

CROP: Conservative Reward for Model-based Offline Policy Optimization

https://github.com/gabrielkmbo/explore-rl

공식 코드

Step-wise Policy for Rare-tool Knowledge (SPaRK): Offline RL that Drives Diverse Tool Use in LLMs

https://github.com/gaoyinfeng/piwm

공식 코드

Dream to Drive with Predictive Individual World Model

https://github.com/gilesluo/reassessdtr

공식 코드

Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination

https://github.com/google-deepmind/dmc_vision_benchmark

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DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors

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

공식 코드

An Optimistic Perspective on Offline Reinforcement Learning

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

공식 코드

Dynamics-Aware Unsupervised Discovery of Skills

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

공식 코드

Behavior Regularized Offline Reinforcement Learning

https://github.com/google-research/google-research/tree/master/experience_replay

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Revisiting Fundamentals of Experience Replay

https://github.com/google-research/google-research/tree/master/multi_game_dt

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Multi-Game Decision Transformers

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

공식 코드

RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

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

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Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

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

공식 코드

Reformer: The Efficient Transformer

https://github.com/guoyihonggyh/mobody-model-based-off-dynamics-offline-reinforcement-learning

공식 코드

MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning

https://github.com/hakuhodo-technologies/scope-rl

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SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation

https://github.com/hanglai/autombpo

공식 코드

On Effective Scheduling of Model-based Reinforcement Learning

https://github.com/hanglai/bmpo

공식 코드

Bidirectional Model-based Policy Optimization

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