{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6nOTljC_mTMn"
   },
   "source": [
    "# SequentialSimulator\n",
    "\n",
    "This python notebook shows the usage of the SequentialSimulator.\n",
    "\n",
    "\n",
    "[![Open In GitHub](https://img.shields.io/badge/see-Github-579aca?logo=github)](https:///github.com/aiplan4eu/unified-planning/blob/master/docs/notebooks/08-sequential-simulator.ipynb)\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aiplan4eu/unified-planning/blob/master/docs/notebooks/08-sequential-simulator.ipynb)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t8dCcpf7mivV"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CwlvEzKrm1jT"
   },
   "source": [
    "First, we install unified_planning library and its dependencies from PyPi. Here, we use the `--pre` flag to use the latest development build."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BoqALxJWdfl8",
    "scrolled": false,
    "tags": [
     "remove_from_CI"
    ]
   },
   "outputs": [],
   "source": [
    "!apt install graphviz graphviz-dev\n",
    "%pip install unified-planning[plot]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iNHFHxQKnKIp"
   },
   "source": [
    "We are now ready to use the Unified-Planning library!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9dP5scv7nNJu"
   },
   "source": [
    "## Demo\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DXhHD7EAmxVx"
   },
   "source": [
    "### Basic imports\n",
    "The basic imports we need for this demo are abstracted in the `shortcuts` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "fiRk2F1llJO-"
   },
   "outputs": [],
   "source": [
    "from unified_planning.shortcuts import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fXA1NuXgkntV"
   },
   "source": [
    "### Problem definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JXDIDNwCm13b"
   },
   "source": [
    "We start the problem modeling defining the `UserType` and the `Fluent`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "nHXzkD2LmWkB"
   },
   "outputs": [],
   "source": [
    "Location = UserType('Location')\n",
    "\n",
    "at = Fluent('at', Location)\n",
    "distance = Fluent('distance', IntType(), l1=Location, l2=Location)\n",
    "battery_charge = Fluent('battery_charge', IntType(0, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OVUn5VPTmXKY"
   },
   "source": [
    "We define an action `move` that decreases the `battery_charge` by the `distance` of the move.\n",
    "\n",
    "Note that the battery can never go under 0, so every action that would reduce the battery to a negative number is not applicable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "cwXh99K2laqv"
   },
   "outputs": [],
   "source": [
    "move = InstantaneousAction('move', l_from=Location, l_to=Location)\n",
    "l_from = move.parameter('l_from')\n",
    "l_to = move.parameter('l_to')\n",
    "move.add_precondition(Equals(at, l_from))\n",
    "move.add_effect(at, l_to)\n",
    "move.add_decrease_effect(battery_charge, distance(l_from, l_to))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C92YH1nrmRb7"
   },
   "source": [
    "Finally, we define the `Object` instances and, after creating the `Problem`, we set the initial values and the goal.\n",
    "\n",
    "Set the default of the `distance` to `101`, so only the distances that we define explicitly create valid connections between locations. (Otherwise, the Typing bound on the `battery_charge` would be violated.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "MczotRommR_k"
   },
   "outputs": [],
   "source": [
    "l1 = Object('l1', Location)\n",
    "l2 = Object('l2', Location)\n",
    "l3 = Object('l3', Location)\n",
    "l4 = Object('l4', Location)\n",
    "l5 = Object('l5', Location)\n",
    "locations = [l5, l4, l3, l2, l1]\n",
    "\n",
    "problem = Problem('moving_robot')\n",
    "problem.add_fluent(at)\n",
    "problem.add_fluent(battery_charge)\n",
    "problem.add_fluent(distance, default_initial_value = 101)\n",
    "problem.add_action(move)\n",
    "problem.add_objects(locations)\n",
    "\n",
    "problem.set_initial_value(at, l1)\n",
    "problem.set_initial_value(battery_charge, 100)\n",
    "\n",
    "problem.set_initial_value(distance(l1, l2), 20)\n",
    "problem.set_initial_value(distance(l2, l3), 30)\n",
    "problem.set_initial_value(distance(l3, l4), 20)\n",
    "problem.set_initial_value(distance(l4, l5), 30)\n",
    "\n",
    "problem.set_initial_value(distance(l1, l3), 60)\n",
    "\n",
    "problem.add_goal(Equals(at, l5))\n",
    "battery_exp = FluentExp(battery_charge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pXvh83ljlabv"
   },
   "source": [
    "### Simulating the problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "McV4znupqpkw"
   },
   "source": [
    "Get the `simulator` with the `SequentialSimulator` Operation Mode and start simulating.\n",
    "\n",
    "Since we have to reach `l5`, we iterate over the locations and see which locations we can reach from `l1` using the `simulator.is_applicable` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "bXHWJh2vl5RJ",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From l1 we can reach: l3\n",
      "From l1 we can reach: l2\n"
     ]
    }
   ],
   "source": [
    "with SequentialSimulator(problem=problem) as simulator:\n",
    "    initial_state = simulator.get_initial_state()\n",
    "    for travel_location in locations:\n",
    "        if simulator.is_applicable(initial_state, move, (l1, travel_location)):\n",
    "            print(f\"From l1 we can reach: {travel_location}\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, from `l1` we can reach `l2` and `l3`; since `l3` seems closer to `l5`, we decide to go there!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From l3 we can reach: l4\n",
      "Problem! Battery too low: 20\n"
     ]
    }
   ],
   "source": [
    "    state_at_l3 = simulator.apply(initial_state, move, (l1, l3))\n",
    "    for travel_location in locations:\n",
    "        if simulator.is_applicable(state_at_l3, move, (l3, travel_location)):\n",
    "            print(f\"From l3 we can reach: {travel_location}\")   \n",
    "    state_at_l4 = simulator.apply(state_at_l3, move, (l3, l4))\n",
    "    if simulator.is_applicable(state_at_l4, move, (l4, l5)):\n",
    "        print('Done!')\n",
    "    else:\n",
    "        print(f'Problem! Battery too low: {state_at_l4.get_value(battery_exp)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found a problem in our simulation. From the state in which we reached `l3`, the remaining battery is not enough to reach `l5`.\n",
    "\n",
    "The only decision we made to go to reach `l3` directly from `l1` seemed wrong, so let's try to reach `l3` passing from `l2`, and see it that saves battery. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reaching l3 passing through l2 saves battery!\n"
     ]
    }
   ],
   "source": [
    "    state_at_l2 = simulator.apply(initial_state, move, (l1, l2))\n",
    "    new_state_at_l3 = simulator.apply(state_at_l2, move, (l2, l3))\n",
    "    new_state_better = (new_state_at_l3.get_value(battery_exp) > state_at_l3.get_value(battery_exp)).simplify()\n",
    "    if new_state_better.bool_constant_value():\n",
    "        print(\"Reaching l3 passing through l2 saves battery!\")\n",
    "    else:\n",
    "        print(\"Can't save battery reaching l3, the problem has no solution!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw, this saves battery, so let's try reaching `l5` with this new battery value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "    state_at_l3 = new_state_at_l3\n",
    "    state_at_l4 = simulator.apply(state_at_l3, move, (l3, l4))\n",
    "    if simulator.is_applicable(state_at_l4, move, (l4, l5)):\n",
    "        print('Done!')\n",
    "    else:\n",
    "        print(f'Problem! Battery too low: {state_at_l4.get_value(battery_exp)}')\n",
    "    state_at_l5 = simulator.apply(state_at_l4, move, (l4, l5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulating a SequentialPlan and inspect the State\n",
    "\n",
    "As we saw, a possible plan is to go trough the Locations in order, so let's create a `SequentialPlan` that does this.\n",
    "\n",
    "After creating the plan, we can simulate it and, for example, save the values of the battery during the whole plan in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial battery value: 100\n",
      "Battery value after move(l1, l2): 80\n",
      "Battery value after move(l2, l3): 50\n",
      "Battery value after move(l3, l4): 30\n",
      "Battery value after move(l4, l5): 0\n"
     ]
    }
   ],
   "source": [
    "    from unified_planning.plans import SequentialPlan, ActionInstance\n",
    "    \n",
    "    plan = SequentialPlan([\n",
    "        ActionInstance(move, (l1, l2)), \n",
    "        ActionInstance(move, (l2, l3)),\n",
    "        ActionInstance(move, (l3, l4)),\n",
    "        ActionInstance(move, (l4, l5))\n",
    "    ])\n",
    "    print(f\"Initial battery value: {initial_state.get_value(battery_exp).constant_value()}\")\n",
    "    current_state = initial_state\n",
    "    # We also store the states to plot the metrics later\n",
    "    states = [current_state]\n",
    "    for action_instance in plan.actions:\n",
    "        current_state = simulator.apply(current_state, action_instance)\n",
    "        if current_state is None:\n",
    "            print(f'Error in applying: {action_instance}')\n",
    "            break\n",
    "        states.append(current_state)\n",
    "        current_battery_value = current_state.get_value(battery_exp).constant_value()\n",
    "        # in current_battery_value we inspect the State\n",
    "        print(f\"Battery value after {action_instance}: {current_battery_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we stored the battery values during the plan, we can do anything with those; in this example we plot the battery values in a graph to see how it decreases at each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    from unified_planning.plot import plot_sequential_plan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore the code below, it's used to make this notebook also runnable in the Countinuous Intergation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    # Redefine the plot package methods imported above to print the plot to a temp file\n",
    "    # if the exception \"could not locate runnable browser\" is raised. This usually happens\n",
    "    # in the Continuous Integration.\n",
    "\n",
    "    from inspect import getmembers, isfunction\n",
    "    from unified_planning import plot\n",
    "    from functools import partial\n",
    "    import os, uuid, tempfile as tf\n",
    "\n",
    "    # Define the function that will be executed instead\n",
    "    def _function(original_function, *args, **kwargs):\n",
    "        try:\n",
    "            original_function(*args, **kwargs)\n",
    "        except Exception as e:\n",
    "            if \"could not locate runnable browser\" in str(e):\n",
    "                original_function(*args, **kwargs,\n",
    "                    filename=f\"{os.path.join(tf.gettempdir(), str(uuid.uuid1()))}.png\"\n",
    "                )\n",
    "            else:\n",
    "                raise e\n",
    "\n",
    "    # Iterate over all the functions of the plot package\n",
    "    for function_name, function in getmembers(plot, isfunction):\n",
    "        # Override the original function with the new one\n",
    "        globals()[function_name] = partial(_function, function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    plot_sequential_plan(plan, problem, battery_exp, figsize=(9, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the Plan Quality Metrics\n",
    "\n",
    "The UP `engines` package offers a utility function that takes a `SequentialSimulator` and evaluates a `PlanQualityMetric`; this is done by using the `State` and the `actions` sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plan length: 1\n",
      "Maximized epression value: 80\n",
      "Plan length: 2\n",
      "Maximized epression value: 50\n",
      "Plan length: 3\n",
      "Maximized epression value: 30\n",
      "Plan length: 4\n",
      "Maximized epression value: 0\n"
     ]
    }
   ],
   "source": [
    "    from unified_planning.engines.sequential_simulator import evaluate_quality_metric, evaluate_quality_metric_in_initial_state\n",
    "\n",
    "    plan_length = MinimizeSequentialPlanLength()\n",
    "    maximize_battery = MaximizeExpressionOnFinalState(battery_exp)\n",
    "\n",
    "    plan_length_value = evaluate_quality_metric_in_initial_state(simulator, plan_length)\n",
    "    maximize_battery_value = evaluate_quality_metric_in_initial_state(simulator, maximize_battery)\n",
    "    \n",
    "    current_state = states[0]\n",
    "    for next_state, action_instance in zip(states[1:], plan.actions):\n",
    "        plan_length_value = evaluate_quality_metric(\n",
    "            simulator, \n",
    "            plan_length, \n",
    "            plan_length_value,\n",
    "            current_state,\n",
    "            action_instance.action,\n",
    "            action_instance.actual_parameters,\n",
    "            next_state\n",
    "        )\n",
    "        maximize_battery_value = evaluate_quality_metric(\n",
    "            simulator, \n",
    "            maximize_battery, \n",
    "            maximize_battery_value,\n",
    "            current_state,\n",
    "            action_instance.action,\n",
    "            action_instance.actual_parameters,\n",
    "            next_state\n",
    "        )\n",
    "        current_state = next_state\n",
    "        \n",
    "        # Do something with the metric values\n",
    "        print(f'Plan length: {plan_length_value}\\nMaximized epression value: {maximize_battery_value}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also the value of the metrics can be plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "    try:\n",
    "        plot_sequential_plan(plan, problem, metric_or_metrics=[plan_length, maximize_battery], figsize=(9, 3))\n",
    "    except Exception as e:\n",
    "        if \"could not locate runnable browser\" in str(e):\n",
    "            plot_sequential_plan(plan, problem, battery_exp, figsize=(9, 3), filename=\"sequential_simulator_2.png\")\n",
    "        else:\n",
    "            raise e"
   ]
  }
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