https://github.com/thomyphan/anytime-mapf
공식 코드Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
Papers with Code 아카이브
Multi-Armed Bandits
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Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.
<span style="color:grey; opacity: 0.6">( Image credit: Microsoft Research )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thomyphan/planning
공식 코드Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
https://github.com/thrungroup/fastforest
공식 코드https://github.com/timnielen/top-k-shapley
공식 코드https://github.com/timo-verstraeten/spts-experiments
공식 코드Scalable Optimization for Wind Farm Control using Coordination Graphs
https://github.com/tung-nd/tnp-pytorch
공식 코드Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
https://github.com/uquh/tsroots
공식 코드Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
https://github.com/usaito/icml2022-mips
공식 코드Off-Policy Evaluation for Large Action Spaces via Embeddings
https://github.com/vanderschaarlab/invconban
공식 코드Inverse Contextual Bandits: Learning How Behavior Evolves over Time
https://github.com/vaswanis/randucb
공식 코드Old Dog Learns New Tricks: Randomized UCB for Bandit Problems
https://github.com/vdblm/ExPerior
공식 코드Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
https://github.com/vectorinstitute/neuralkernelbandits
공식 코드Empirical analysis of representation learning and exploration in neural kernel bandits
https://github.com/vidits-kth/LinConTS
공식 코드https://github.com/vub-ai-lab/bdpi
공식 코드Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
https://github.com/wesselb/NeuralProcesses.jl
공식 코드Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
https://github.com/wilrev/lowrankbanditstwotoinfinity
공식 코드Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery
https://github.com/windxrz/smaa
공식 코드Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
https://github.com/xiwenchao/conTS-TOIS-2021
공식 코드Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
https://github.com/xuyunbei/mab-code
공식 코드Bayesian Design Principles for Frequentist Sequential Learning
https://github.com/yhjung88/ThompsonSamplinginRestlessBandits
공식 코드Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems
https://github.com/yiwei0129/fast-beam-alignment
공식 코드Fast Beam Alignment via Pure Exploration in Multi-armed Bandits
https://github.com/ymy4323460/HATCH
공식 코드Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation
https://github.com/yunzhe-zhou/PBL
공식 코드Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
https://github.com/yusonghust/HierarchicyBandit
공식 코드https://github.com/zcc1307/warmcb_scripts
공식 코드Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
https://github.com/zhangry868/S2VGD
공식 코드Learning Structural Weight Uncertainty for Sequential Decision-Making
https://github.com/zhiwang123/eps-mpmab-ts
공식 코드https://github.com/zhiyongwangwzy/conlinucb
공식 코드Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits
https://github.com/zihaophys/topk_colt23
공식 코드https://github.com/zwd-ms/risk_averse_cb
공식 코드https://gitlab.com/vanparys/dusa
공식 코드https://gitlab.control.lth.se/FridaH/mab_for_bci-public
공식 코드https://github.com/2023-MindSpore-1/ms-code-220/tree/main/ldp_linucb
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_03_linear-networks
https://github.com/BBloggsbott/k-armed-bandits
https://github.com/Byte7/Adaptative-Curriculum-GAN-keras
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.