a High-Performance Distributed Solver for Large-Scale Markov Decision Processes (MDP) relying on Inexact Policy Iteration; for Python and C++
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Updated
Sep 25, 2024 - C++
a High-Performance Distributed Solver for Large-Scale Markov Decision Processes (MDP) relying on Inexact Policy Iteration; for Python and C++
Symbolic compilation of RDDL domains, Dynamic Bayes net (DBN) visualization, symbolic dynamic programming (SDP).
University course exercises
Benchmarking Distributed Inexact Policy Iteration for Large-Scale Markov Decision Processes
Artificial Intelligence course, Computer Science M.Sc., Ben Gurion University of the Negev, 2021
This repository contains a practical application of Infinite Horizon Dynamic Programming (IHDP) techniques, demonstrated through the Frozen Lake environment and grid world examples. The repository includes a Jupyter Notebook that explores these techniques with visual aids.
This repo contains implementation of algorithms that I have learned in my course work of Reinforcment learning
Agent which computes the optimal policy for in a Dice Game
Reinforcement Learning and Deeep reinforcement Learning
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