Papers with Code 아카이브

Decision Making

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코드

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

https://github.com/illidanlab/sail

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

https://github.com/imagination-research/lbt

Reflexion: Language Agents with Verbal Reinforcement Learning

https://github.com/infiagent/infiagent

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/instadeepai/marl-eval

Towards a Standardised Performance Evaluation Protocol for Cooperative MARL

https://github.com/integrated-intelligence-lab/canonical_sets

LUCID: Exposing Algorithmic Bias through Inverse Design

https://github.com/intelligent-environments-lab/CityLearn

Soft Actor-Critic Algorithms and Applications

https://github.com/interpretml/DiCE

Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

https://github.com/intuitive-robots/MoDE_Diffusion_Policy

Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning

https://github.com/intuitive-robots/beso

Goal-Conditioned Imitation Learning using Score-based Diffusion Policies

https://github.com/irom-lab/dragen

Generalization Guarantees for Imitation Learning

https://github.com/irskid5/drr_restaurants

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

https://github.com/issamlaradji/ssr

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

https://github.com/jaara/classification-with-costly-features

Classification with Costly Features as a Sequential Decision-Making Problem

https://github.com/jackal-u/CSGOAPI

Counter-Strike Deathmatch with Large-Scale Behavioural Cloning

https://github.com/jaesik817/visual-interaction-networks_tensorflow

Visual Interaction Networks

https://github.com/jaideepmurkute/Multi-purpose-Disentangling-Variational-Autoencoders-for-ECG-data

Improving Disentangled Representation Learning with the Beta Bernoulli Process

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/grac.py

GRAC: Self-Guided and Self-Regularized Actor-Critic

https://github.com/jakegrigsby/deep_control/blob/master/deep_control/sac.py

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/janisgp/Sampling-free-Epistemic-Uncertainty

Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

https://github.com/jcoreyes/lgpl

Guiding Policies with Language via Meta-Learning

https://github.com/jemaw/gym-safety

A Lyapunov-based Approach to Safe Reinforcement Learning

https://github.com/jenspetersen/probabilistic-unet

A Probabilistic U-Net for Segmentation of Ambiguous Images

https://github.com/jerrylin1121/BCO

Behavioral Cloning from Observation

https://github.com/jessefokkinga/MechanismsOfAction

TabNet: Attentive Interpretable Tabular Learning

https://github.com/jessevig/bertviz

Attention is not not Explanation

https://github.com/jfpettit/jfpettit.github.io

Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning

https://github.com/jhejna/dwsl

Distance Weighted Supervised Learning for Offline Interaction Data

https://github.com/jhu-lcsr/costar_plan

Visual Robot Task Planning

https://github.com/jianhuig/fairmetrics

What is Fair? Defining Fairness in Machine Learning for Health

https://github.com/jiechuanjiang/pytorch_dgn

Graph Convolutional Reinforcement Learning

https://github.com/jja-bot/jamiegogo/blob/master/layouts/partials/contact.html

PeRView: A Framework for Personalized Review Selection Using Micro-Reviews

https://github.com/jjccero/pbrl/tree/master/pbrl/algorithms/sac

Soft Actor-Critic Algorithms and Applications

https://github.com/jkbren/ps-gpucbpe

Optimal design of experiments to identify latent behavioral types

https://github.com/joeloskarsson/neural-lam

GraphCast: Learning skillful medium-range global weather forecasting

https://github.com/joeloskarsson/neural-lam-dev

GraphCast: Learning skillful medium-range global weather forecasting

https://github.com/jozhang97/side-tuning

Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks

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