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

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

Sum: 755.

Methods

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.

update

Update an arm's state.

Parameters

• arm_id
• reward_args
• reward_kwargs