{ "cells": [ { "cell_type": "markdown", "id": "e11b09f4-f9e3-48db-af38-59f9d8bec88b", "metadata": {}, "source": [ "# 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." ] }, { "cell_type": "code", "execution_count": 1, "id": "85002218-89fe-4c6b-9b5a-cd1b9c65dfff", "metadata": { "execution": { "iopub.execute_input": "2025-07-02T09:44:47.126825Z", "iopub.status.busy": "2025-07-02T09:44:47.126605Z", "iopub.status.idle": "2025-07-02T09:44:48.213659Z", "shell.execute_reply": "2025-07-02T09:44:48.213129Z" } }, "outputs": [], "source": [ "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=True) -> 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" ] }, { "cell_type": "code", "execution_count": 2, "id": "9c164688-b7a0-43bd-8a93-25aa2b93d49d", "metadata": { "execution": { "iopub.execute_input": "2025-07-02T09:44:48.215594Z", "iopub.status.busy": "2025-07-02T09:44:48.215308Z", "iopub.status.idle": "2025-07-02T09:44:54.395133Z", "shell.execute_reply": "2025-07-02T09:44:54.394630Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "be49434043c24d25ba1fd0d812778eba", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "function return_id_result(url, id, data) {\n", " fetch(url, {\n", " method: 'POST',\n", " body: JSON.stringify({\n", " 'id': id,\n", " 'data': data\n", " })\n", " })\n", " }\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "return_id_result('http://127.0.0.1:8889', 'qpTodIsXGeAnNXoYhxlK', 'test message')" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Test request failed. See 'Communication server remark' in docs. Disable warning by use_server=False.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cd42b89a847044bf99ee2cc99911af9d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f79aa91f434943b9acd7ba8932fa886f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3045f6b513404720897d1588b11ef993", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dbded05c6fdc40c48cb74607b68354e1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b597210ccdb34b8fb316f6c71100ed99", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Value iteration done:\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cb5c73bd9b8046ac9bffbdb2aaaf1952", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "af4bae5b170147b8b419f49d6a552f20", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", " \n", " Network\n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", " \n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lion = examples.create_lion_mdp()\n", "prop = 'P=?[F \"full\"]'\n", "res = policy_iteration(lion, prop, layout=Layout(\"layouts/lion_policy.json\"))" ] }, { "cell_type": "markdown", "id": "f786fbad-2fc9-42e1-9659-17ee72a0c477", "metadata": {}, "source": [ "Policy iteration is also available under `stormvogel.extensions.visual_algos`." ] }, { "cell_type": "code", "execution_count": null, "id": "4f21e277-1c2a-4d42-9b60-c06630bf78bd", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "08b016f06f3b4e79b2fda4c6f997e2fb": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/output", "_model_module_version": "1.0.0", "_model_name": "OutputModel", "_view_count": null, "_view_module": "@jupyter-widgets/output", "_view_module_version": "1.0.0", "_view_name": "OutputView", "layout": "IPY_MODEL_b0d32fc311df4d07967e6fc517a79d9b", "msg_id": "", "outputs": [ { "data": { "text/html": "\n\n\n \n Network\n \n \n \n \n \n
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