MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.
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Updated
Jul 28, 2024 - Julia
MDPs and POMDPs in Julia - An interface for defining, solving, and simulating fully and partially observable Markov decision processes on discrete and continuous spaces.
Implementation of the Deep Q-learning algorithm to solve MDPs
Online solver based on Monte Carlo tree search for POMDPs with continuous state, action, and observation spaces.
A gallery of POMDPs.jl problems
The PO-UCT algorithm (aka POMCP) implemented in Julia
Concise and friendly interfaces for defining MDP and POMDP models for use with POMDPs.jl solvers
Adaptive stress testing of black-box systems within POMDPs.jl
Compressed belief-state MDPs in Julia compatible with POMDPs.jl
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