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Sequential Decision Making

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코드

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

https://github.com/pjhool/TF-A2RL-Test

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

https://github.com/qayqaq/lanmdp

Learning non-Markovian Decision-Making from State-only Sequences

https://github.com/ravi-lanka-4/CoPiEr

Co-training for Policy Learning

https://github.com/richemslie/galvanise_zero

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/robfiras/ls-iq

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/sb-ai-lab/RePlay

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

https://github.com/shadowyzy/NIPS2019-MineRL-Competition-solution

The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

https://github.com/shalijiang/bo

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

https://github.com/shashist/recsys-rl

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

https://github.com/sholtodouglas/pointMass

Learning Multi-Level Hierarchies with Hindsight

https://github.com/snasiriany/leap

Planning with Goal-Conditioned Policies

https://github.com/sudh1976/ECE510_DeepLearning_Project

An Alternative Softmax Operator for Reinforcement Learning

https://github.com/sunritc/FLIPHAT

FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits

https://github.com/sunyasheng/a2rl_pytorch

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

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

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

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/thompsonj/nanoGCG

An Alternative Softmax Operator for Reinforcement Learning

https://github.com/tristandeleu/gfn-maxent-rl

Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

https://github.com/tsingqaq/trieste

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

https://github.com/vectorinstitute/neuralkernelbandits

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

https://github.com/wmkouw/ssa-nlp

Back to the Future -- Sequential Alignment of Text Representations

https://github.com/wongongv/scnwithdqn

Structured Control Nets for Deep Reinforcement Learning

https://github.com/xavierpuigf/virtualhome

Pre-Trained Language Models for Interactive Decision-Making

https://github.com/yashchandak/SAS_RL

Reinforcement Learning When All Actions are Not Always Available

https://github.com/yashchandak/lifelong_changing_actions

Lifelong Learning with a Changing Action Set

https://github.com/yenchehsiao/autonomousllmagentwithadaptingplanning

Reflexion: Language Agents with Verbal Reinforcement Learning

https://github.com/ymlwww/rlproject

Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward

https://github.com/yoojungsun0/Psych239

Model-Free Episodic Control

https://github.com/yufeiwang63/L2D-learning-to-discretize

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

https://github.com/zivaharoni/capacity-rl

Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning

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