An Automata Learning Library Written in Python
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Updated
Jun 20, 2024 - Python
An Automata Learning Library Written in Python
A research platform to develop automated security policies using quantitative methods, e.g., optimal control, computational game theory, reinforcement learning, optimization, evolutionary methods, and causal inference.
Seasons of Code 2024 (Mentee) Project
This project implements a Markov Decision Process (MDP) using Reinforcement Learning in Python.
Implementation of the MDP algorithm for optimal decision-making, focusing on value iteration and policy determination.
AWS Last Mile Route Sequence Optimization
Code for "Optimizing ZX-Diagrams with Deep Reinforcement Learning"
Several RL-agents are tested on classical environments and benchmarked against their stable-baselines implementation.
Repository for the final project for Procesos Estocásticos. S1.63.10
A framework to build and solve POMDP problems. Documentation: https://h2r.github.io/pomdp-py/
A Dynamic Programming package for discrete MDPs implemented in JAX
Framework for the simulation and estimation of some finite-horizon discrete choice dynamic programming models.
A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
Project that experiments with algorithms used to solve Markov Decision Processes
Baum-Welch for all kind of Markov models
Repo for maze generation and pathfinding algorithms, including BFS, DFS, A*, MDP Value Iteration, and MDP Policy Iteration, implemented in Python for solving mazes.
Monte 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 …
A simple reinforcement learning project which solves a simple garbage collecting problem with Markov Decision Process.
This project utilizes Markov Decision Process (MDP) principles to implement a custom "CliffWalking" environment in Gym, employing policy iteration to find an optimal policy for agent navigation.
This repository contains the codes for Term Projects as part of the Reinforcement Learning course (CS600077) that I am taking in the Autumn 2023 semester at IIT Kharagpur
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