From 7dc8a4266141e2b6ea7191c55e1c26b8b38ce9bd Mon Sep 17 00:00:00 2001
From: Dylan Asmar <asmar@stanford.edu>
Date: Fri, 18 Oct 2024 12:49:08 -0600
Subject: [PATCH] Updated jldoctest to use StableRNGs in def_pomdp.md

---
 docs/Project.toml     | 1 +
 docs/src/def_pomdp.md | 6 +++---
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/docs/Project.toml b/docs/Project.toml
index d714848f..2a2f8757 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -21,6 +21,7 @@ ParticleFilters = "c8b314e2-9260-5cf8-ae76-3be7461ca6d0"
 QMDP = "3aa3ecc9-5a5d-57c8-8188-3e47bd8068d2"
 QuickPOMDPs = "8af83fb2-a731-493c-9049-9e19dbce6165"
 RockSample = "de008ff0-c357-11e8-3329-7fe746fe836e"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 TagPOMDPProblem = "8a653263-a1cc-4cf9-849f-f530f6ffc800"
 UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
diff --git a/docs/src/def_pomdp.md b/docs/src/def_pomdp.md
index 03b0198f..6bff1462 100644
--- a/docs/src/def_pomdp.md
+++ b/docs/src/def_pomdp.md
@@ -216,15 +216,15 @@ In many cases, especially when the state or observation spaces are continuous or
 The argument to an `ImplicitDistribution` constructor is a function that takes a random number generator as an argument and returns a sample from the distribution. To see how this works, we'll look at an example inspired by the [mountaincar](@ref po-mountaincar) initial state distribution.
 Samples from this distribution are position-velocity tuples where the velocity is always zero, but the position is uniformly distributed between -0.2 and 0. Consider the following code:
 ```jldoctest
-using Random: MersenneTwister
+using StableRNGs: StableRNG
 using POMDPTools: ImplicitDistribution
 
-rng = MersenneTwister(1)
+rng = StableRNG(1)
 
 d = ImplicitDistribution(rng -> (-0.2*rand(rng), 0.0))
 rand(rng, d)
 # output
-(-0.04720666913240939, 0.0)
+(-0.11703892844248372, 0.0)
 ```
 Here, `rng` is the random number generator. When `rand(rng, d)` is called, the sampling function, `rng -> (-0.2*rand(rng), 0.0)`, is called to generate a state.  The sampling function uses `rng` to generate a random number between 0 and 1 (`rand(rng)`), multiplies it by -0.2 to get the position, and creates a tuple with the position and a velocity of `0.0` and returns an initial state that might be, for instance `(-0.11, 0.0)`. Any time that a solver, belief updater, or simulator needs an initial state for the problem, it will be sampled in this way.