Grids, mountains, and mysterious problems. Solved with Partially-Observable Markov Decision Procesees. Created at Stanford University, by Pablo Rodriguez Bertorello
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Updated
Nov 7, 2017 - Julia
Grids, mountains, and mysterious problems. Solved with Partially-Observable Markov Decision Procesees. Created at Stanford University, by Pablo Rodriguez Bertorello
Reinforcement Learning in Julia (Experimental)
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Concise and friendly interfaces for defining MDP and POMDP models for use with POMDPs.jl solvers
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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