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
Jun 18, 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.
Stochastic Dual Dynamic Programming in Julia
Concise and friendly interfaces for defining MDP and POMDP models for use with POMDPs.jl solvers
Compute value function for a shale driller's problem
Reinforcement Learning in Julia (Experimental)
Compressed belief-state MDPs in Julia compatible with POMDPs.jl
Grids, mountains, and mysterious problems. Solved with Partially-Observable Markov Decision Procesees. Created at Stanford University, by Pablo Rodriguez Bertorello
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