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active-learning-reinforcement-learning

This code can be used to reproduce the results in our paper ``Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach''.

control-importance-distribution-algorithm-cida-

This code can be used to reproduce the results in our paper ``A Control Approach for Nonlinear Stochastic State Uncertain Systems with Probabilistic Safety Guarantees''.

data-conforming-control

This code can be used to reproduce the results in our paper ``Data-conforming data-driven control: avoiding premature generalizations beyond data''

dataenabledcontrol.jl

This is the Julia implementation of the behavioral control DeePC algorithm.

efficient-particle-filter-oop

A particle filter as a class and its particles and their likelihoods are attributes. The measurement- and time-update steps are listed as methods.

extended-kalman-filter-usingpytorchautograd

Uses PyTorch tensors and autograd | The extended Kalman filtering algorithm as a class: mean and covariance are attributes and propagation in time are methods | PyTorch autograd is used to return the jacobians of the state and measurement dynamics | Example.py presents a quick demo.

fixedpointem_algorithm

This code can be used to reproduce the results in our paper ``Maximum Likelihood recursive state estimation using the Expectation Maximization algorithm''.

linearizing-uncertainty-for-control

This code can be used to reproduce the results in our paper ``Linearizing uncertainties that matter for control: the Koopman operator in the dual control context''.

montecarlo_dynamicprogramming

Monte Carlo Grid Dynamic Programming: Almost Sure Convergence and Probability Constraints

msramada

msramada.github.io

near-expert-warmup-reinforcement-learning

Reinforcement learning algorithm initialized by supervised learning of a nearly expert agent.

on-the-unit-commitment-problems

policy-iteration-markov-decision-process-oop

Policy iteration algorithm applied on finite state and action spaces Markov decision process.

reinforcement-learning-with-active-learning-pytorch

Using ideas from stochastic optimal control in reinforcement learning.

state-selection-algorithm

The Python codes, of the algorithms for linear and nonlinear systems, used in our paper``State estimation for control: an approach for output-feedback stochastic MPC''.