Compressed belief-state MDPs in Julia compatible with POMDPs.jl
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Updated
Jul 24, 2024 - Julia
Compressed belief-state MDPs in Julia compatible with POMDPs.jl
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.
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
A framework to build and solve POMDP problems. Documentation: https://h2r.github.io/pomdp-py/
Adaptive stress testing of black-box systems within POMDPs.jl
A C++ framework for MDPs and POMDPs with Python bindings
A collection of pomdp domains in robotics.
Interface for defining discrete and continuous-space MDPs and POMDPs in python. Compatible with the POMDPs.jl ecosystem.
A gallery of POMDPs.jl problems
Pytorch code for "Learning Belief Representations for Imitation Learning in POMDPs" (UAI 2019)
Julia Implementation of the POMCP algorithm for solving POMDPs
A POMDP solver using Littman-Cassandra's Witness algorithm.
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