fixed version of https://github.com/JuliaPOMDP/POMDPs.jl/discussions/513

python_pomcpow.py

from julia.CommonRLSpaces import Box

from julia.Main import Float64
from julia.POMDPs import solve, pdf,action
from julia.QMDP import QMDPSolver
from julia.POMCPOW import POMCPOWSolver
from julia.POMDPTools import stepthrough, alphavectors, Uniform, Deterministic
from julia.Distributions import Normal,AbstractMvNormal,MvNormal

from quickpomdps import QuickPOMDP

import numpy as np

goal = [3,3,0]
def reward(s, a, sp):
    if s[0] == goal[0] and s[1] == goal[1]:
        return 5.0
    else:
        return -1.0

def transition(s, a, dt=0.1):
    s0 = s[0] + a[0] * np.sin(s[2]) * dt
    s1 = s[1] + a[0] * np.sin(s[2]) * dt
    return MvNormal([s0, s1, s[2]], [0.1,0.1,0.1])


def observation(s, a, sp):
    # sp is next state
    return MvNormal(sp, [0.001,0.001,0.001])

def reward(s, a, sp):
    if s[0] == goal[0] and s[1] == goal[1]:
        return 5.0
    else:
        return -1.0

m = QuickPOMDP(
    states = Box([-5,-5,-3], [5,5,3]),
    actions = Box([-5,-5], [5,5]),
    observations=Box([-5,-5,-3], [5,5,3]),
    discount = 0.9,
    isterminal = lambda s: (s[0] == goal[0] and s[1] == goal[1]),
    transition = transition,
    observation = observation,
    reward = reward,
    initialstate = Deterministic([0.0,0.0,0.0])
)
solver = POMCPOWSolver()
#solver = QMDPSolver()
policy = solve(solver, m)
a = action(policy,Deterministic([0.0,0.0,0.0]))
print(a)