51 skills found · Page 1 of 2
opendilab / LightZero[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
Grid2op / Grid2opGrid2Op a testbed platform to model sequential decision making in power systems.
microsoft / SeqMLA repository containing the implementations about our machine learning researches on sequence data and sequential decision making.
TuSimple / Rl Multishot ReidMulti-shot Pedestrian Re-identification via Sequential Decision Making (CVPR2018)
nyu-dl / Dl4ir WebnavWebNav: A New Large-Scale Task for Natural Language based Sequential Decision Making
masouduut94 / MCTS Agent PythonMonte Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a search tree accordingly. It has already had a profound impact on Artificial Intelligence (AI) approaches for domains that can be represented as trees of sequential decisions, particularly games and planning problems. In this project I used a board game called "HEX" as a platform to test different simulation strategies in MCTS field.
rebase-energy / Enflow⚡ Open-source Python framework for modelling sequential decision problems in the energy sector
donghun2018 / Seqdecisionlib ReleaseSequential Decision Problem Modeling Library @ Castle Lab, Princeton Univ.
micahcarroll / UniMASKCodebase for "Uni[MASK]: Unified Inference in Sequential Decision Problems"
sisl / SCoBA.jlStochastic Conflict-Based Allocation
markkho / MsdmModels of Sequential Decision-Making
hammer-wang / Awesome Transformers For Sequential Decision MakingTracking literature and additional online resources on transformers for sequential decision making including RL and beyond.
ShortestPathLab / Posthoc AppPosthoc is a way to build simple and effective visualisations ✨ for sequential decision-making algorithms, such as search.
Geraldine-Winston / Petroleum Reservoir Property Prediction Using LSTM Networks On Well Log Data.This project uses LSTM networks to predict reservoir properties like porosity from sequential well log data, enabling improved reservoir characterization and aiding petroleum engineers in making better exploration and production decisions.
gerdm / Martingale Posterior Neural NetworksMartingale posterior neural networks for fast sequential decision making @ Neurips 2025
brownirl / Lambda DiscrepancyMitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
Sinbad-The-Sailor / AbacusAutomatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
Intelligent-Driving-Laboratory / Reinforcement Learning For Sequential Decision And Optimal ControlSource Code for "Reinforcement Learning for Sequential Decision and Optimal Control" by Shengbo Eben Li
Human-Centric-Machine-Learning / Counterfactual Explanations MdpCode for "Counterfactual Explanations in Sequential Decision Making Under Uncertainty", NeurIPS 2021
