Papers with Code 아카이브

Reinforcement Learning

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코드

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

https://github.com/emi-group/gpbt-pl

공식 코드

Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement Learning

https://github.com/emily-halina/brisket

공식 코드

Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI

https://github.com/emptyjackson/unifloral

공식 코드

A Clean Slate for Offline Reinforcement Learning

https://github.com/emtiyaz/vadam

공식 코드

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

https://github.com/emunaran/stochastic-human-driving-policies-drl

공식 코드

Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars

https://github.com/enajx/HebbianMetaLearning

공식 코드

Meta-Learning through Hebbian Plasticity in Random Networks

https://github.com/enjeeneer/PEARL

공식 코드

Low Emission Building Control with Zero-Shot Reinforcement Learning

https://github.com/enjeeneer/conservative-world-models

공식 코드

Zero-Shot Reinforcement Learning from Low Quality Data

https://github.com/enjeeneer/zero-shot-rl

공식 코드

Zero-Shot Reinforcement Learning from Low Quality Data

https://github.com/epfl-imos/sym-q

공식 코드

Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework

https://github.com/epignatelli/navix

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NAVIX: Scaling MiniGrid Environments with JAX

https://github.com/episodeyang/e-maml

공식 코드

Some Considerations on Learning to Explore via Meta-Reinforcement Learning

https://github.com/ercbunny/isaacgymenvs

공식 코드

Learning Generalizable Policy for Obstacle-Aware Autonomous Drone Racing

https://github.com/ercumentilhan/advice-imitation-reuse

공식 코드

Learning on a Budget via Teacher Imitation

https://github.com/ercumentilhan/advice-novelty

공식 코드

Student-Initiated Action Advising via Advice Novelty

https://github.com/ercumentilhan/naive-advice-imitation

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Action Advising with Advice Imitation in Deep Reinforcement Learning

https://github.com/erg0dic/transmon_public

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Sample-efficient Model-based Reinforcement Learning for Quantum Control

https://github.com/ergoncugler/q-learning

공식 코드

From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no Libraries

https://github.com/eric-mitchell/macaw

공식 코드

Offline Meta-Reinforcement Learning with Advantage Weighting

https://github.com/ericjiang18/sdsra

공식 코드

SDSRA: A Skill-Driven Skill-Recombination Algorithm for Efficient Policy Learning

https://github.com/ericotjo001/srd

공식 코드

Self Reward Design with Fine-grained Interpretability

https://github.com/ericyangyu/pocar

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Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems

https://github.com/erik-derner/symbolic-regression

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Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression

https://github.com/ermongroup/MA-AIRL

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Multi-Agent Adversarial Inverse Reinforcement Learning

https://github.com/ermongroup/MetaIRL

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Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

https://github.com/ermongroup/PatchDrop

공식 코드

Learning When and Where to Zoom with Deep Reinforcement Learning

https://github.com/ermongroup/higher_order_invariance

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Accelerating Natural Gradient with Higher-Order Invariance

https://github.com/ernie-research/cd-rlhf

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Curiosity-Driven Reinforcement Learning from Human Feedback

https://github.com/ernie-research/ma-rlhf

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MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions

https://github.com/ernovoseller/DuelingPosteriorSampling

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Dueling Posterior Sampling for Preference-Based Reinforcement Learning

https://github.com/eserie/wax-ml

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WAX-ML: A Python library for machine learning and feedback loops on streaming data

https://github.com/eslam211/conservative-and-distributional-marl

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Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning

https://github.com/esraaelelimy/gtd_algos

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Deep Reinforcement Learning with Gradient Eligibility Traces

https://github.com/esraaelelimy/rtus

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Real-Time Recurrent Learning using Trace Units in Reinforcement Learning

https://github.com/etalvitie/hdaggermc

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Self-Correcting Models for Model-Based Reinforcement Learning

https://github.com/ethancjackson/NoveltySearchLevenshtein

공식 코드

Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance

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