https://github.com/namkoong-lab/data-recipes
공식 코드Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
Papers with Code 아카이브
Sequential Decision Making
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/neildhir/dcbo
공식 코드https://github.com/networks-learning/counterfactual-continuous-mdp
공식 코드Finding Counterfactually Optimal Action Sequences in Continuous State Spaces
https://github.com/networks-learning/counterfactual-explanations-mdp
공식 코드Counterfactual Explanations in Sequential Decision Making Under Uncertainty
https://github.com/nick-jhlee/bandits-network
공식 코드Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks
https://github.com/noahshinn024/reflexion
공식 코드Reflexion: Language Agents with Verbal Reinforcement Learning
https://github.com/nrel/rlc4clr
공식 코드Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration
https://github.com/nudt-bi-marl/pmat
공식 코드PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning
https://github.com/nyu-dl/WebNav
공식 코드https://github.com/ondrejba/discrete_abstractions
공식 코드Learning Discrete State Abstractions With Deep Variational Inference
https://github.com/opendilab/ace
공식 코드ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
https://github.com/parisadavar/EVT-policy-gradient-RL
공식 코드Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients
https://github.com/pearl-utexas/sope
공식 코드https://github.com/pendu/containergym
공식 코드ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation
https://github.com/pengyang7881187/sicmdp-new
공식 코드Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
https://github.com/pkassraie/NNUCB
공식 코드https://github.com/pku-marl/multi-agent-transformer
공식 코드Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
https://github.com/pmineiro/linrepcb
공식 코드https://github.com/praal/remembering-to-be-fair
공식 코드Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making
https://github.com/premiertaco/premier-taco
공식 코드https://github.com/qiliuchn/data-corruption-study
공식 코드https://github.com/quester-one/awesome-llm-planning
공식 코드Large Language Models for Planning: A Comprehensive and Systematic Survey
https://github.com/rafaol/batch-bore-with-guarantees
공식 코드Batch Bayesian optimisation via density-ratio estimation with guarantees
https://github.com/raise-lab/soc-nav-training
공식 코드Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
https://github.com/raunakkmr/non-monotonic-resource-utilization-in-the-bandits-with-knapsacks-problem-code
공식 코드Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem
https://github.com/riccardodv/coop-learning
공식 코드https://github.com/richardhgl/chi2025_plan-then-execute_llmagent
공식 코드https://github.com/rimichen/collaborative_comic_generation
공식 코드https://github.com/rothkopflab/nioc-neurips
공식 코드https://github.com/ryanchankh/VariationalInformationPursuit
공식 코드Variational Information Pursuit for Interpretable Predictions
https://github.com/rzayanov/irl-teaching-limited-feedback
공식 코드Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback
https://github.com/saeedghoorchian/ncc-bandits
공식 코드Online Learning with Costly Features in Non-stationary Environments
https://github.com/scope-lab-vu/PAMCTS
공식 코드Decision Making in Non-Stationary Environments with Policy-Augmented Search
https://github.com/scope-lab-vu/ada-mcts
공식 코드Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
https://github.com/seanrsinclair/hindsight-learning
공식 코드https://github.com/secondmind-labs/trieste
공식 코드Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.