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- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "IntSliderModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "IntSliderView",
- "behavior": "drag-tap",
- "continuous_update": true,
- "description": "Mass",
- "description_allow_html": false,
- "disabled": false,
- "layout": "IPY_MODEL_bebc6c0210c6434eb08ece2ad508cbb1",
- "max": 100,
- "min": 0,
- "orientation": "horizontal",
- "readout": true,
- "readout_format": "d",
- "step": 1,
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- "tabbable": null,
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- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "2.0.0",
- "model_name": "VBoxModel",
- "state": {
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- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "2.0.0",
- "_model_name": "VBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "2.0.0",
- "_view_name": "VBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_a4c94a80ed104375b3beba921299fa2c"
- ],
- "layout": "IPY_MODEL_f3deccb1f2624689a63564ee5f767f0f",
- "tabbable": null,
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- }
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- "ffa1f039d87a4fdca63e6095d428b7bd": {
- "model_module": "@jupyter-widgets/base",
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- "_model_name": "LayoutModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "2.0.0",
- "_view_name": "LayoutView",
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- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
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- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
- }
- }
- },
- "version_major": 2,
- "version_minor": 0
- }
+ "version": "3.12.7"
}
},
"nbformat": 4,
diff --git a/docs/getting_started/simulator.ipynb b/docs/getting_started/simulator.ipynb
new file mode 100644
index 0000000..0dbb8d1
--- /dev/null
+++ b/docs/getting_started/simulator.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ddc37c-66d2-43e4-8162-6be19a1d70a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from stormvogel import visualization, show, simulator\n",
+ "import stormvogel.model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cab40f99-3460-4497-8b9f-3d669eee1e11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# We create the monty hall mdp\n",
+ "mdp = stormvogel.model.new_mdp(\"Monty Hall\")\n",
+ "\n",
+ "init = mdp.get_initial_state()\n",
+ "\n",
+ "# first choose car position\n",
+ "init.set_transitions(\n",
+ " [(1 / 3, mdp.new_state(\"carchosen\", {\"car_pos\": i})) for i in range(3)]\n",
+ ")\n",
+ "\n",
+ "# we choose a door in each case\n",
+ "for s in mdp.get_states_with_label(\"carchosen\"):\n",
+ " s.set_transitions(\n",
+ " [\n",
+ " (\n",
+ " mdp.action(f\"open{i}\"),\n",
+ " mdp.new_state(\"open\", s.features | {\"chosen_pos\": i}),\n",
+ " )\n",
+ " for i in range(3)\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "# the other goat is revealed\n",
+ "for s in mdp.get_states_with_label(\"open\"):\n",
+ " car_pos = s.features[\"car_pos\"]\n",
+ " chosen_pos = s.features[\"chosen_pos\"]\n",
+ " other_pos = {0, 1, 2} - {car_pos, chosen_pos}\n",
+ " s.set_transitions(\n",
+ " [\n",
+ " (\n",
+ " 1 / len(other_pos),\n",
+ " mdp.new_state(\"goatrevealed\", s.features | {\"reveal_pos\": i}),\n",
+ " )\n",
+ " for i in other_pos\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "# we must choose whether we want to switch\n",
+ "for s in mdp.get_states_with_label(\"goatrevealed\"):\n",
+ " car_pos = s.features[\"car_pos\"]\n",
+ " chosen_pos = s.features[\"chosen_pos\"]\n",
+ " reveal_pos = s.features[\"reveal_pos\"]\n",
+ " other_pos = list({0, 1, 2} - {reveal_pos, chosen_pos})[0]\n",
+ " s.set_transitions(\n",
+ " [\n",
+ " (\n",
+ " mdp.action(\"stay\"),\n",
+ " mdp.new_state(\n",
+ " [\"done\"] + ([\"target\"] if chosen_pos == car_pos else []),\n",
+ " s.features | {\"chosen_pos\": chosen_pos},\n",
+ " ),\n",
+ " ),\n",
+ " (\n",
+ " mdp.action(\"switch\"),\n",
+ " mdp.new_state(\n",
+ " [\"done\"] + ([\"target\"] if other_pos == car_pos else []),\n",
+ " s.features | {\"chosen_pos\": other_pos},\n",
+ " ),\n",
+ " ),\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "# we add self loops to all states with no outgoing transitions\n",
+ "mdp.add_self_loops()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb0fadc0-7bb6-4c1d-ae3e-9e16527726ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#we want to simulate this model. That is, we start at the initial state and then\n",
+ "#we walk through the model according to transition probabilities.\n",
+ "#When we do this, we get a partial model as a result that contains everything we discovered\n",
+ "#during this walk.\n",
+ "\n",
+ "#we can choose how many steps we take:\n",
+ "steps = 4\n",
+ "\n",
+ "#and we can specify a seed if we want:\n",
+ "seed = 123456\n",
+ "\n",
+ "#then we run the simulator:\n",
+ "partial_model = stormvogel.simulator.simulate(mdp, steps=steps, seed=seed)\n",
+ "print(partial_model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "59ac1e34-866c-42c4-b19b-c2a15c830e2e",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "#it still chooses random actions but we can prevent this by providing a scheduler:\n",
+ "taken_actions = {}\n",
+ "for id, state in mdp.states.items():\n",
+ " taken_actions[id] = state.available_actions()[0]\n",
+ "scheduler = stormvogel.result.Scheduler(mdp, taken_actions)\n",
+ "\n",
+ "partial_model = stormvogel.simulator.simulate(mdp, steps=steps, scheduler=scheduler, seed=seed)\n",
+ "print(partial_model)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22871288-755c-463f-9150-f207c2f5c211",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#we can also visualize the partial model that we get from the simulator:\n",
+ "show.show(partial_model)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "34d0c293-d090-4e3d-9e80-4351f5fcba62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#we can also use another simulator function that returns a path instead of a partial model:\n",
+ "path = stormvogel.simulator.simulate_path(mdp, steps=4, scheduler=scheduler, seed=123456)\n",
+ "\n",
+ "print(path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "99c763fa-82ea-42ff-8833-79c640f14518",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python (stormvogel)",
+ "language": "python",
+ "name": "stormvogel-env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/monty_hall_pomdp.py b/examples/monty_hall_pomdp.py
index 6324925..b7b4159 100644
--- a/examples/monty_hall_pomdp.py
+++ b/examples/monty_hall_pomdp.py
@@ -69,7 +69,7 @@ def create_monty_hall_pomdp():
# we add the observations TODO: let it make sense
for state in pomdp.states.values():
- state.set_observation(0)
+ state.set_observation(state.id)
# test if the normalize function works
pomdp.normalize()
diff --git a/poetry.lock b/poetry.lock
index 3082d79..c53feeb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3436,4 +3436,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
-content-hash = "65bbdf4a8a22412ec0f95ebe412b3ec8a4821be723e154dfa0a80c8dc73ccc46"
+content-hash = "c7ce7fa5e4ee787e2296e46d57bce7e48c2fd7f8a8b848f6b3a06de03989023f"
diff --git a/pyproject.toml b/pyproject.toml
index a84074c..f265a76 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,6 +23,7 @@ matplotlib = "^3.9.2"
anyio = "^4.6.0"
nbmake = "^1.5.4"
mock = "^5.1.0"
+jupyter = "^1.1.1"
[tool.poetry.group.test.dependencies]
pytest = "^8.2.2"
@@ -38,7 +39,6 @@ jupyterlab = "^4.2.2"
sphinx = "^7.3.7"
furo = "^2024.5.6"
nbsphinx = "^0.9.4"
-jupyter = "^1.0.0"
[tool.ruff]
src = ["stormvogel", "examples"]
diff --git a/stormvogel/model.py b/stormvogel/model.py
index ef9cd3e..1f7da00 100644
--- a/stormvogel/model.py
+++ b/stormvogel/model.py
@@ -306,7 +306,7 @@ def transition_from_shorthand(shorthand: TransitionShorthand) -> Transition:
)
-@dataclass
+@dataclass(order=True)
class RewardModel:
"""Represents a state-exit reward model.
dtmc.delete_state(dtmc.get_state_by_id(1), True, True)
@@ -504,12 +504,10 @@ def set_transitions(self, s: State, transitions: Transition | TransitionShorthan
def add_transitions(self, s: State, transitions: Transition | TransitionShorthand):
"""Add new transitions from a state. If no transition currently exists, the result will be the same as set_transitions."""
- if not self.supports_actions():
- raise RuntimeError(
- "Models without actions do not support add_transitions. Use set_transitions instead."
- )
+
if not isinstance(transitions, Transition):
transitions = transition_from_shorthand(transitions)
+
try:
existing_transitions = self.get_transitions(s)
except KeyError:
@@ -517,30 +515,35 @@ def add_transitions(self, s: State, transitions: Transition | TransitionShorthan
self.set_transitions(s, transitions)
return
- # Adding a transition is only valid if they are both empty or both non-empty.
- if (
- not transitions.has_empty_action()
- and existing_transitions.has_empty_action()
- ):
- raise RuntimeError(
- "You cannot add a transition with an non-empty action to a transition which has an empty action. Use set_transition instead."
- )
- if (
- transitions.has_empty_action()
- and not existing_transitions.has_empty_action()
- ):
- raise RuntimeError(
- "You cannot add a transition with an empty action to a transition which has no empty action. Use set_transition instead."
- )
-
- # Empty action case, add the branches together.
- if transitions.has_empty_action():
- self.transitions[s.id].transition[EmptyAction] += transitions.transition[
+ if not self.supports_actions():
+ self.transitions[s.id].transition[
EmptyAction
- ]
+ ].branch += transitions.transition[EmptyAction].branch
else:
- for choice, branch in transitions.transition.items():
- self.transitions[s.id].transition[choice] = branch
+ # Adding a transition is only valid if they are both empty or both non-empty.
+ if (
+ not transitions.has_empty_action()
+ and existing_transitions.has_empty_action()
+ ):
+ raise RuntimeError(
+ "You cannot add a transition with an non-empty action to a transition which has an empty action. Use set_transition instead."
+ )
+ if (
+ transitions.has_empty_action()
+ and not existing_transitions.has_empty_action()
+ ):
+ raise RuntimeError(
+ "You cannot add a transition with an empty action to a transition which has no empty action. Use set_transition instead."
+ )
+
+ # Empty action case, add the branches together.
+ if transitions.has_empty_action():
+ self.transitions[s.id].transition[EmptyAction] += (
+ transitions.transition[EmptyAction]
+ )
+ else:
+ for choice, branch in transitions.transition.items():
+ self.transitions[s.id].transition[choice] = branch
def get_transitions(self, state_or_id: State | int) -> Transition:
"""Get the transition at state s. Throws a KeyError if not present."""
@@ -842,9 +845,10 @@ def __eq__(self, other):
self.type == other.type
and self.states == other.states
and self.transitions == other.transitions
- and self.rewards == other.rewards
+ and sorted(self.rewards) == sorted(other.rewards)
and self.exit_rates == other.exit_rates
and self.markovian_states == other.markovian_states
+ # TODO: and self.actions == other.actions
)
return False
@@ -872,3 +876,8 @@ def new_pomdp(name: str | None = None):
def new_ma(name: str | None = None):
"""Creates a MA."""
return Model(name, ModelType.MA)
+
+
+def new_model(modeltype: ModelType, name: str | None = None):
+ """More general model creation function"""
+ return Model(name, modeltype)
diff --git a/stormvogel/simulator.py b/stormvogel/simulator.py
index 965a2c0..408d33e 100644
--- a/stormvogel/simulator.py
+++ b/stormvogel/simulator.py
@@ -5,39 +5,302 @@
import stormvogel.model
import stormpy.examples.files
import stormpy.examples
-import examples.die
+import random
-def simulate(
- model: stormvogel.model.Model, steps: int = 1, runs: int = 1
-) -> stormvogel.model.Model:
+class Path:
"""
- Simulates the model a given number of steps for a given number of runs.
- Returns the partial model discovered by the simulator
+ Path object that represents a path created by a simulator on a certain model.
+
+ Args:
+ path: The path itself is a dictionary where we either store for each step a state or a state action pair,
+ depending on if we are working with a dtmc or an mdp.
+ model: model that the path traverses through
"""
- stormpy_model = stormvogel.mapping.stormvogel_to_stormpy(model)
- simulator = stormpy.simulator.create_simulator(stormpy_model)
- partial_model = stormvogel.model.new_model(model.get_type())
+ path: (
+ dict[int, tuple[stormvogel.model.Action, stormvogel.model.State]]
+ | dict[int, stormvogel.model.State]
+ )
+ model: stormvogel.model.Model
+
+ def __init__(
+ self,
+ path: dict[int, tuple[stormvogel.model.Action, stormvogel.model.State]]
+ | dict[int, stormvogel.model.State],
+ model: stormvogel.model.Model,
+ ):
+ if model.get_type() != stormvogel.model.ModelType.MA:
+ self.path = path
+ self.model = model
+ else:
+ # TODO make the simulators work for markov automata
+ raise NotImplementedError
+
+ def get_state_in_step(self, step: int) -> stormvogel.model.State | None:
+ """returns the state discovered in the given step in the path"""
+ if not self.model.supports_actions():
+ state = self.path[step]
+ assert isinstance(state, stormvogel.model.State)
+ return state
+ if self.supports_actions():
+ t = self.path[step]
+ assert (
+ isinstance(t, tuple)
+ and isinstance(t[0], stormvogel.model.Action)
+ and isinstance(t[1], stormvogel.model.State)
+ )
+ state = t[1]
+ assert isinstance(state, stormvogel.model.State)
+ return state
+
+ def get_action_in_step(self, step: int) -> stormvogel.model.Action | None:
+ """returns the action discovered in the given step in the path"""
+ if self.model.supports_actions():
+ t = self.path[step]
+ assert (
+ isinstance(t, tuple)
+ and isinstance(t[0], stormvogel.model.Action)
+ and isinstance(t[1], stormvogel.model.State)
+ )
+ action = t[0]
+ return action
+
+ def get_step(
+ self, step: int
+ ) -> (
+ tuple[stormvogel.model.Action, stormvogel.model.State] | stormvogel.model.State
+ ):
+ """returns the state or state action pair discovered in the given step"""
+ return self.path[step]
+
+ def __str__(self) -> str:
+ path = "initial state"
+ if self.model.supports_actions():
+ for t in self.path.values():
+ assert (
+ isinstance(t, tuple)
+ and isinstance(t[0], stormvogel.model.Action)
+ and isinstance(t[1], stormvogel.model.State)
+ )
+ path += f" --(action: {t[0].name})--> state: {t[1].id}"
+ else:
+ for state in self.path.values():
+ path += f" --> state: {state.id}"
+ return path
+
+ def __eq__(self, other):
+ if isinstance(other, Path):
+ return self.path == other.path and self.model == other.model
+ else:
+ return False
+
+
+def simulate_path(
+ model: stormvogel.model.Model,
+ steps: int = 1,
+ scheduler: stormvogel.result.Scheduler | None = None,
+ seed: int | None = None,
+) -> Path:
+ """
+ Simulates the model and returns the path created by the process.
+ Args:
+ model: The stormvogel model that the simulator should run on.
+ steps: The number of steps the simulator walks through the model.
+ scheduler: A stormvogel scheduler to determine what actions should be taken. Random if not provided.
+ seed: The seed for the function that determines for each state what the next state will be. Random seed if not provided.
+ Returns a path object.
+ """
+
+ def get_range_index(stateid: int):
+ """Helper function to convert the chosen action in a state by a scheduler to a range index."""
+ assert scheduler is not None
+ action = scheduler.get_choice_of_state(model.get_state_by_id(state))
+ available_actions = model.states[stateid].available_actions()
+
+ assert action is not None
+ return available_actions.index(action)
+
+ # we initialize the simulator
+ stormpy_model = stormvogel.mapping.stormvogel_to_stormpy(model)
+ if seed:
+ simulator = stormpy.simulator.create_simulator(stormpy_model, seed)
+ else:
+ simulator = stormpy.simulator.create_simulator(stormpy_model)
assert simulator is not None
- discovered_states = []
- for i in range(runs):
+
+ # we start adding states or state action pairs to the path
+ if not model.supports_actions():
+ path = {}
simulator.restart()
- for j in range(steps):
+ for i in range(steps):
+ # for each step we add a state to the path
state, reward, labels = simulator.step()
- if state not in discovered_states:
- partial_model.new_state(list(labels))
- discovered_states.append(state)
+ path[i + 1] = model.states[state]
if simulator.is_done():
break
+ else:
+ state = 0
+ path = {}
+ simulator.restart()
+ for i in range(steps):
+ # we first choose an action (randomly or according to scheduler)
+ actions = simulator.available_actions()
+ select_action = (
+ random.randint(0, len(actions) - 1)
+ if not scheduler
+ else get_range_index(state)
+ )
- return partial_model
+ # we add the state action pair to the path
+ stormvogel_action = model.states[state].available_actions()[select_action]
+ next_step = simulator.step(actions[select_action])
+ state, reward, labels = next_step
+ path[i + 1] = (stormvogel_action, model.states[state])
+ if simulator.is_done():
+ break
+
+ path_object = Path(path, model)
+
+ return path_object
-if __name__ == "__main__":
- dtmc = examples.die.create_die_dtmc()
+def simulate(
+ model: stormvogel.model.Model,
+ steps: int = 1,
+ runs: int = 1,
+ scheduler: stormvogel.result.Scheduler | None = None,
+ seed: int | None = None,
+) -> stormvogel.model.Model | None:
+ """
+ Simulates the model.
+ Args:
+ model: The stormvogel model that the simulator should run on
+ steps: The number of steps the simulator walks through the model
+ runs: The number of times the model gets simulated.
+ scheduler: A stormvogel scheduler to determine what actions should be taken. Random if not provided.
+ seed: The seed for the function that determines for each state what the next state will be. Random seed if not provided.
+
+ Returns the partial model discovered by all the runs of the simulator together
+ """
+
+ def get_range_index(stateid: int):
+ """Helper function to convert the chosen action in a state by a scheduler to a range index."""
+ assert scheduler is not None
+ action = scheduler.get_choice_of_state(model.get_state_by_id(state))
+ available_actions = model.states[stateid].available_actions()
+
+ assert action is not None
+ return available_actions.index(action)
+
+ # we initialize the simulator
+ stormpy_model = stormvogel.mapping.stormvogel_to_stormpy(model)
+ assert stormpy_model is not None
+ if seed:
+ simulator = stormpy.simulator.create_simulator(stormpy_model, seed)
+ else:
+ simulator = stormpy.simulator.create_simulator(stormpy_model)
+ assert simulator is not None
+
+ # we keep track of all discovered states over all runs and add them to the partial model
+ # we also add the discovered rewards and actions to the partial model if present
+ partial_model = stormvogel.model.new_model(model.get_type())
+
+ # we add each rewardmodel to the partial model
+ if model.rewards:
+ for index, reward in enumerate(model.rewards):
+ reward_model = partial_model.add_rewards(model.rewards[index].name)
+
+ # we already set the rewards for the initial state
+ reward_model.set(
+ partial_model.get_initial_state(),
+ model.rewards[index].get(model.get_initial_state()),
+ )
- partial_model = simulate(dtmc, 1, 100)
+ # now we start stepping through the model
+ discovered_states = {0}
+ if not partial_model.supports_actions():
+ for i in range(runs):
+ simulator.restart()
+ last_state = 0
+ for j in range(steps):
+ state, reward, labels = simulator.step()
+ reward.reverse()
- print(partial_model)
+ # we add to the partial model what we discovered (if new)
+ if state not in discovered_states:
+ discovered_states.add(state)
+
+ # we also add the transitions that we travelled through, so we need to keep track of the last state
+ probability = 0
+ transitions = model.get_transitions(last_state)
+ for tuple in transitions.transition[
+ stormvogel.model.EmptyAction
+ ].branch:
+ if tuple[1].id == state:
+ probability += float(tuple[0])
+
+ new_state = partial_model.new_state(list(labels))
+ partial_model.get_state_by_id(last_state).add_transitions(
+ [(probability, new_state)]
+ )
+
+ for index, rewardmodel in enumerate(partial_model.rewards):
+ rewardmodel.set(new_state, reward[index])
+
+ last_state = state
+ if simulator.is_done():
+ break
+ else:
+ state = 0
+ last_state_partial = partial_model.get_initial_state()
+ last_state_id = 0
+ for i in range(runs):
+ simulator.restart()
+ for j in range(steps):
+ # we first choose an action
+ actions = simulator.available_actions()
+ select_action = (
+ random.randint(0, len(actions) - 1)
+ if not scheduler
+ else get_range_index(state)
+ )
+
+ # we add the action to the partial model
+ assert partial_model.actions is not None
+ action = model.states[state].available_actions()[select_action]
+ if action not in partial_model.actions.values():
+ partial_model.new_action(action.name)
+
+ # we add the reward model to the partial model
+ discovery = simulator.step(actions[select_action])
+ reward = discovery[1]
+ for index, rewardmodel in enumerate(partial_model.rewards):
+ row_group = stormpy_model.transition_matrix.get_row_group_start(
+ state
+ )
+ state_action_pair = row_group + select_action
+ rewardmodel.set_action_state(state_action_pair, reward[index])
+
+ # we add the state
+ state, labels = discovery[0], discovery[2]
+ if state not in discovered_states:
+ discovered_states.add(state)
+
+ # we also add the transitions that we travelled through, so we need to keep track of the last state
+ probability = 0
+ transitions = model.get_transitions(last_state_id)
+ for tuple in transitions.transition[action].branch:
+ if tuple[1].id == state:
+ probability += float(tuple[0])
+
+ new_state = partial_model.new_state(list(labels))
+ last_state_partial.add_transitions([(probability, new_state)])
+
+ last_state_partial = new_state
+ last_state_id = state
+ if simulator.is_done():
+ break
+
+ return partial_model
diff --git a/tests/test_mapping.py b/tests/test_mapping.py
index 0dba539..847178f 100644
--- a/tests/test_mapping.py
+++ b/tests/test_mapping.py
@@ -110,6 +110,15 @@ def test_stormpy_to_stormvogel_and_back_dtmc():
def test_stormvogel_to_stormpy_and_back_dtmc():
# we test it for the die dtmc
stormvogel_dtmc = examples.die.create_die_dtmc()
+
+ # we test if rewardmodels work:
+ rewardmodel = stormvogel_dtmc.add_rewards("rewardmodel")
+ for stateid in stormvogel_dtmc.states.keys():
+ rewardmodel.rewards[stateid] = 1
+ rewardmodel2 = stormvogel_dtmc.add_rewards("rewardmodel2")
+ for stateid in stormvogel_dtmc.states.keys():
+ rewardmodel2.rewards[stateid] = 2
+
# print(stormvogel_dtmc)
stormpy_dtmc = stormvogel.mapping.stormvogel_to_stormpy(stormvogel_dtmc)
# print(stormpy_dtmc)
@@ -136,6 +145,15 @@ def test_stormpy_to_stormvogel_and_back_mdp():
def test_stormvogel_to_stormpy_and_back_mdp():
# we test it for monty hall mdp
stormvogel_mdp = examples.monty_hall.create_monty_hall_mdp()
+
+ # we additionally test if reward models work
+ rewardmodel = stormvogel_mdp.add_rewards("rewardmodel")
+ for i in range(67):
+ rewardmodel.rewards[i] = i
+ rewardmodel2 = stormvogel_mdp.add_rewards("rewardmodel2")
+ for i in range(67):
+ rewardmodel2.rewards[i] = i
+
# print(stormvogel_mdp)
stormpy_mdp = stormvogel.mapping.stormvogel_to_stormpy(stormvogel_mdp)
# print(stormpy_mdp)
diff --git a/tests/test_model_methods.py b/tests/test_model_methods.py
index 2b3dee1..ca51655 100644
--- a/tests/test_model_methods.py
+++ b/tests/test_model_methods.py
@@ -1,5 +1,6 @@
import stormvogel.model
import examples.monty_hall
+import examples.die
import examples.nuclear_fusion_ctmc
import pytest
from typing import cast
@@ -170,15 +171,15 @@ def test_add_transitions():
dtmc = stormvogel.model.new_dtmc()
state = dtmc.new_state()
# A non-action model should throw an exception.
- with pytest.raises(RuntimeError) as excinfo:
- dtmc.add_transitions(
- dtmc.get_initial_state(),
- [(0.5, state)],
- )
- assert (
- str(excinfo.value)
- == "Models without actions do not support add_transitions. Use set_transitions instead."
- )
+ # with pytest.raises(RuntimeError) as excinfo:
+ # dtmc.add_transitions(
+ # dtmc.get_initial_state(),
+ # [(0.5, state)],
+ # )
+ # assert (
+ # str(excinfo.value)
+ # == "Models without actions do not support add_transitions. Use set_transitions instead."
+ # )
# Empty transition case, act exactly like set_transitions.
mdp = stormvogel.model.new_mdp()
@@ -242,6 +243,6 @@ def test_add_transitions():
action6b = mdp6.new_action("b")
mdp6.set_transitions(mdp6.get_initial_state(), [(action6a, state6)])
mdp6.add_transitions(mdp6.get_initial_state(), [(action6b, state6)])
- print(mdp6.get_transitions(mdp6.get_initial_state()).transition)
- print([(action6a, state6), (action6b, state6)])
+ # print(mdp6.get_transitions(mdp6.get_initial_state()).transition)
+ # print([(action6a, state6), (action6b, state6)])
assert len(mdp6.get_transitions(mdp6.get_initial_state()).transition) == 2
diff --git a/tests/test_simulator.py b/tests/test_simulator.py
new file mode 100644
index 0000000..b593956
--- /dev/null
+++ b/tests/test_simulator.py
@@ -0,0 +1,120 @@
+import stormvogel.model
+import examples.die
+import examples.nuclear_fusion_ctmc
+import stormvogel.simulator
+
+
+def test_simulate():
+ # we make a die dtmc and run the simulator with it
+ dtmc = examples.die.create_die_dtmc()
+ rewardmodel = dtmc.add_rewards("rewardmodel")
+ for stateid in dtmc.states.keys():
+ rewardmodel.rewards[stateid] = 3
+ rewardmodel2 = dtmc.add_rewards("rewardmodel2")
+ for stateid in dtmc.states.keys():
+ rewardmodel2.rewards[stateid] = 2
+ rewardmodel3 = dtmc.add_rewards("rewardmodel3")
+ for stateid in dtmc.states.keys():
+ rewardmodel3.rewards[stateid] = 1
+ partial_model = stormvogel.simulator.simulate(dtmc, runs=5, steps=1, seed=1)
+
+ # we make the partial model that should be created by the simulator
+ other_dtmc = stormvogel.model.new_dtmc()
+ other_dtmc.get_initial_state().set_transitions(
+ [
+ (1 / 6, other_dtmc.new_state("rolled5")),
+ (1 / 6, other_dtmc.new_state("rolled0")),
+ (1 / 6, other_dtmc.new_state("rolled1")),
+ ]
+ )
+
+ rewardmodel = other_dtmc.add_rewards("rewardmodel")
+ for stateid in other_dtmc.states.keys():
+ rewardmodel.rewards[stateid] = float(3)
+ rewardmodel2 = other_dtmc.add_rewards("rewardmodel2")
+ for stateid in other_dtmc.states.keys():
+ rewardmodel2.rewards[stateid] = float(2)
+ rewardmodel3 = other_dtmc.add_rewards("rewardmodel3")
+ for stateid in other_dtmc.states.keys():
+ rewardmodel3.rewards[stateid] = float(1)
+
+ assert partial_model == other_dtmc
+
+ # we make a monty hall mdp and run the simulator with it
+ mdp = examples.monty_hall.create_monty_hall_mdp()
+ rewardmodel = mdp.add_rewards("rewardmodel")
+ for i in range(67):
+ rewardmodel.rewards[i] = i
+ rewardmodel2 = mdp.add_rewards("rewardmodel2")
+ for i in range(67):
+ rewardmodel2.rewards[i] = i
+
+ taken_actions = {}
+ for id, state in mdp.states.items():
+ taken_actions[id] = state.available_actions()[0]
+ scheduler = stormvogel.result.Scheduler(mdp, taken_actions)
+
+ partial_model = stormvogel.simulator.simulate(
+ mdp, runs=1, steps=3, seed=1, scheduler=scheduler
+ )
+
+ # we make the partial model that should be created by the simulator
+ other_mdp = stormvogel.model.new_mdp()
+ other_mdp.get_initial_state().set_transitions(
+ [(1 / 3, other_mdp.new_state("carchosen"))]
+ )
+ other_mdp.get_state_by_id(1).set_transitions([(1, other_mdp.new_state("open"))])
+ other_mdp.get_state_by_id(2).set_transitions(
+ [(1, other_mdp.new_state("goatrevealed"))]
+ )
+
+ rewardmodel = other_mdp.add_rewards("rewardmodel")
+ rewardmodel.rewards = {0: 0, 7: 7, 16: 16}
+ rewardmodel2 = other_mdp.add_rewards("rewardmodel2")
+ rewardmodel2.rewards = {0: 0, 7: 7, 16: 16}
+
+ assert partial_model == other_mdp
+
+
+def test_simulate_path():
+ # we make the nuclear fusion ctmc and run simulate path with it
+ ctmc = examples.nuclear_fusion_ctmc.create_nuclear_fusion_ctmc()
+ path = stormvogel.simulator.simulate_path(ctmc, steps=5, seed=1)
+
+ # we make the path that the simulate path function should create
+ other_path = stormvogel.simulator.Path(
+ {
+ 1: ctmc.get_state_by_id(1),
+ 2: ctmc.get_state_by_id(2),
+ 3: ctmc.get_state_by_id(3),
+ 4: ctmc.get_state_by_id(4),
+ },
+ ctmc,
+ )
+
+ assert path == other_path
+
+ # we make the monty hall pomdp and run simulate path with it
+ pomdp = examples.monty_hall_pomdp.create_monty_hall_pomdp()
+ taken_actions = {}
+ for id, state in pomdp.states.items():
+ taken_actions[id] = state.available_actions()[
+ len(state.available_actions()) - 1
+ ]
+ scheduler = stormvogel.result.Scheduler(pomdp, taken_actions)
+ path = stormvogel.simulator.simulate_path(
+ pomdp, steps=4, seed=1, scheduler=scheduler
+ )
+
+ # we make the path that the simulate path function should create
+ other_path = stormvogel.simulator.Path(
+ {
+ 1: (stormvogel.model.EmptyAction, pomdp.get_state_by_id(3)),
+ 2: (pomdp.actions["open2"], pomdp.get_state_by_id(12)),
+ 3: (stormvogel.model.EmptyAction, pomdp.get_state_by_id(23)),
+ 4: (pomdp.actions["switch"], pomdp.get_state_by_id(46)),
+ },
+ pomdp,
+ )
+
+ assert path == other_path