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Decision Making

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

https://github.com/jsyzeng/rock_class_xgboost

Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation

https://github.com/jungguchoi/quantum_annealing_based_feature_selection

Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer

https://github.com/junzhin/dgm-vlc

HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis

https://github.com/justinchiu/NeuralDialog

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

https://github.com/justinjfu/inverse_rl

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution

Soft Actor-Critic Algorithms and Applications

https://github.com/jwallnoefer/gym-teleportation

Machine learning for long-distance quantum communication

https://github.com/kairproject/kair_algorithms_draft

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

https://github.com/kaist-ailab/imitation-dice

LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation

https://github.com/kaistAI/LangBridge

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

https://github.com/kalebbennaveed/Trajectory-Planning-for-Autonomous-Vehicles-Using-HRL

Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

https://github.com/kallekku/hips

Hierarchical Imitation Learning with Vector Quantized Models

https://github.com/karim-abdel/fact

Explaining RL Decisions with Trajectories

https://github.com/katamb/tree-of-thought-llm-ca

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

https://github.com/katejarne/RRN_dynamics

Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks

https://github.com/katejarne/excitatory-inhibitory

Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks

https://github.com/kdally/fault-tolerant-flight-control-drl

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

https://github.com/kenjyoung/MinAtar

The Arcade Learning Environment: An Evaluation Platform for General Agents

https://github.com/keras-team/keras-io/blob/master/examples/vision/temporal_latent_bottleneck.py

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

https://github.com/kevinkamm/aquaculturestochasticmortality

On the Impact of Feeding Cost Risk in Aquaculture Valuation and Decision Making

https://github.com/khalil-research/neur2bilo

Neur2SP: Neural Two-Stage Stochastic Programming

https://github.com/khalil-research/pyepo

Contrastive Losses and Solution Caching for Predict-and-Optimize

https://github.com/kherud/neural-additive-models-pt

Neural Additive Models: Interpretable Machine Learning with Neural Nets

https://github.com/kilgore92/Probabalistic-U-Net

A Probabilistic U-Net for Segmentation of Ambiguous Images

https://github.com/kirangpcet/KRCCTumor

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

https://github.com/kjboggess/ijcai23

Toward Policy Explanations for Multi-Agent Reinforcement Learning

https://github.com/knowledgetechnologyuhh/goal_conditioned_RL_baselines

Learning Multi-Level Hierarchies with Hindsight

https://github.com/ksetdekov/trip_choice_optimizer

Thompson Sampling Algorithms for Mean-Variance Bandits

https://github.com/ku2482/discor.pytorch

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

https://github.com/ku2482/gail-airl-ppo.pytorch

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

https://github.com/ku2482/rljax

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

https://github.com/ku2482/rltorch

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

https://github.com/ku2482/soft-actor-critic.pytorch

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

https://github.com/kushagra06/SAC

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

https://github.com/kushaltirumala/callibratable_style_consistency_flies

Learning Calibratable Policies using Programmatic Style-Consistency

https://github.com/lanqingli1993/focal-iclr

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.