# Reward Models¶

In Getting Started, we mainly looked at probabilities in the Markov models and properties that refer to these probabilities. In this section, we discuss reward models.

## Exploring reward models¶

We consider the die again, but with another property which talks about the expected reward:

```
[1]:
```

```
>>> import stormpy
>>> import stormpy.examples
>>> import stormpy.examples.files
>>> program = stormpy.parse_prism_program(stormpy.examples.files.prism_dtmc_die)
>>> prop = "R=? [F \"done\"]"
>>> properties = stormpy.parse_properties(prop, program, None)
>>> model = stormpy.build_model(program, properties)
>>> assert len(model.reward_models) == 1
```

The model now has a reward model, as the property talks about rewards. When Building Models from explicit sources, the reward model is always included if it is defined in the source. We can do model checking analogous to probabilities:

```
[2]:
```

```
>>> initial_state = model.initial_states[0]
>>> result = stormpy.model_checking(model, properties[0])
>>> print("Result: {}".format(round(result.at(initial_state), 6)))
```

```
Result: 3.666667
```

The reward model has a name which we can obtain as follows:

```
[3]:
```

```
>>> reward_model_name = list(model.reward_models.keys())[0]
>>> print(reward_model_name)
```

```
coin_flips
```

We discuss later how to work with multiple reward models. Rewards come in multiple fashions, as state rewards, state-action rewards and as transition rewards. In this example, we only have state-action rewards. These rewards are a vector, over which we can trivially iterate:

```
[4]:
```

```
>>> assert not model.reward_models[reward_model_name].has_state_rewards
>>> assert model.reward_models[reward_model_name].has_state_action_rewards
>>> assert not model.reward_models[reward_model_name].has_transition_rewards
>>> for reward in model.reward_models[reward_model_name].state_action_rewards:
... print(reward)
```

```
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.0
0.0
0.0
0.0
0.0
0.0
```