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"# Policy iteration\n",
"In policy iteration, you start with an arbitrary policy.\n",
"Then, the the policy is improved at every iteration by first creating a DTMC for the previous policy, and then applying whichever choice would be best in that DTMC for the updated policy."
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"from stormvogel import *\n",
"from stormvogel.visualization import Visualization\n",
"from time import sleep\n",
"\n",
"def arg_max(funcs, args):\n",
" \"\"\"Takes a list of callables and arguments and return the argument that yields the highest value.\"\"\"\n",
" executed = [f(x) for f,x in zip(funcs,args)]\n",
" index = executed.index(max(executed))\n",
" return args[index]\n",
"\n",
"def policy_iteration(\n",
" model: Model,\n",
" prop: str,\n",
" visualize: bool = True,\n",
" layout: Layout = stormvogel.layout.DEFAULT(), \n",
" delay:int=2,\n",
" clear:bool=False) -> Result:\n",
" \"\"\"Performs policy iteration on the given mdp.\n",
" Args:\n",
" model (Model): MDP.\n",
" prop (str): PRISM property string to maximize. Rembember that this is a property on the induced DTMC, not the MDP.\n",
" visualize (bool): Whether the intermediate and final results should be visualized. Defaults to True.\n",
" layout (Layout): Layout to use to show the intermediate results.\n",
" delay (int): Seconds to wait between each iteration.\n",
" clear (bool): Whether to clear the visualization of each previous iteration.\n",
" \"\"\"\n",
" old = None\n",
" new = random_scheduler(model)\n",
"\n",
" while not old == new:\n",
" old = new\n",
"\n",
" dtmc = old.generate_induced_dtmc()\n",
" dtmc_result = model_checking(dtmc, prop=prop)\n",
"\n",
" if visualize:\n",
" vis = Visualization(model, layout=layout, scheduler=old, result=dtmc_result)\n",
" vis.show()\n",
" sleep(delay)\n",
" if clear:\n",
" vis.clear()\n",
"\n",
" choices = {i: \n",
" arg_max(\n",
" [lambda a: sum([(p * dtmc_result.get_result_of_state(s2.id)) for p, s2 in s1.get_outgoing_transitions(a)])\n",
" for _ in s1.available_actions()], \n",
" s1.available_actions())\n",
" for i,s1 in model.get_states().items()}\n",
" new = Scheduler(model, choices)\n",
" if visualize:\n",
" print(\"Value iteration done:\")\n",
" show(model, layout=layout, scheduler=new, result=dtmc_result)\n",
" return dtmc_result"
]
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"lion = examples.create_lion_mdp()\n",
"prop = 'P=?[F \"full\"]'\n",
"res = policy_iteration(lion, prop, layout=Layout(\"layouts/lion_policy.json\"))"
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