# RandomPolicy

Random bandit policy.

This policy simply pulls a random arm at each time step. It is useful as a baseline.

## Parameters

- **reward\_obj**

  *Default* → `None`

  The reward object that is used to update the posterior distribution.
- **burn\_in**

  *Default* → `0`

  Number of initial observations per arm before using the posterior distribution.
- **seed**

  *Type* → `int | None`

  *Default* → `None`

  Random number generator seed for reproducibility.

## Attributes

- **ranking**

  Return the list of arms in descending order of performance.

## Examples

```python
import gymnasium as gym
from river import bandit
from river import proba
from river import stats

env = gym.make(
    'river_bandits/CandyCaneContest-v0'
)
_ = env.reset(seed=42)
_ = env.action_space.seed(123)

policy = bandit.RandomPolicy(seed=123)

metric = stats.Sum()
while True:
    action = policy.pull(range(env.action_space.n))
    observation, reward, terminated, truncated, info = env.step(action)
    policy.update(action, reward)
    metric.update(reward)
    if terminated or truncated:
        break

metric
```

```text
Sum: 755.
```

## Methods

> [!NOTE]
>
> **pull**
>
> Pull arm(s).
>
> This method is a generator that yields the arm(s) that should be pulled. During the burn-in phase, all the arms that have not been pulled enough times are yielded. Once the burn-in phase is over, the policy is allowed to choose the arm(s) that should be pulled. If you only want to pull one arm at a time during the burn-in phase, simply call `next(policy.pull(arms))`.
>
> **Parameters**
>
> - **arm\_ids** — `list[ArmID]`
>
> **Returns**
>
> `ArmID`: A single arm.

> [!NOTE]
>
> **update**
>
> Update an arm's state.
>
> **Parameters**
>
> - **arm\_id**
> - **reward\_args**
> - **reward\_kwargs**