Papers with Code 아카이브

Decision Making

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

2

보존된 평가 기록

2

코드

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

https://github.com/sunyasheng/a2rl_pytorch

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

https://github.com/susan-amin/SparseBaseline1

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

https://github.com/susumuota/gym-modeestimation

Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning

https://github.com/suvadityamuk/Temporal-Latent-Bottleneck-TF

Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning

https://github.com/sydney-machine-learning/pingala

Surrogate-assisted parallel tempering for Bayesian neural learning

https://github.com/syzhang092218-source/Confidence-Aware-Imitation-Learning

Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality

https://github.com/tabayashi0117/Score-CAM

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

https://github.com/tamamin/Affect-Adjusted-NLP

The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting

https://github.com/tarod13/SAC

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

https://github.com/tccnchsu/study

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

https://github.com/tedmoskovitz/DOPE

Benchmarks for Deep Off-Policy Evaluation

https://github.com/tedmoskovitz/TOP

Benchmarks for Deep Off-Policy Evaluation

https://github.com/tedmoskovitz/constrainedrl4lms

Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

https://github.com/tensorflow/models

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

https://github.com/tensorflow/models/tree/archive/research/deep_contextual_bandits

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

https://github.com/tensorlayer/RLzoo

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

https://github.com/tenvinc/CS4246-Project

Deep Q-learning from Demonstrations

https://github.com/thaihungle/SHM

Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning

https://github.com/theamrzaki/text_summurization_abstractive_methods

Deep Reinforcement Learning For Sequence to Sequence Models

https://github.com/theodorewolf/climate_policy_rl

Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models

https://github.com/thezingaro/multimodal-side-tuning

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

https://github.com/thomasaunger/babyai

BabyAI 1.1

https://github.com/thomashirtz/pytorch-soft-actor-critic

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

https://github.com/thomashirtz/soft-actor-critic

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

https://github.com/thompsonj/nanoGCG

An Alternative Softmax Operator for Reinforcement Learning

https://github.com/thorschoepe/collisionavoidance_NRP

Finding the Gap: Neuromorphic Motion Vision in Cluttered Environments

https://github.com/thudm/agenttuning

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/thumnlab/autogl-light

Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search

https://github.com/tianheyu927/mil

One-Shot Visual Imitation Learning via Meta-Learning

https://github.com/tilkb/thermoai

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

https://github.com/timoklein/car_racer

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

https://github.com/titu1994/tf-TabNet

TabNet: Attentive Interpretable Tabular Learning

https://github.com/tju-drl-lab/self-supervised-rl

PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations

https://github.com/tkipf/gym-gridworld

CompILE: Compositional Imitation Learning and Execution

https://github.com/tkusmierczyk/correcting_approximate_bayesian_predictions

Correcting Predictions for Approximate Bayesian Inference

https://github.com/tliu1997/rnac

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

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