UCB¶

Upper Confidence Bound (UCB) bandit policy.

Due to the nature of this algorithm, it's recommended to scale the target so that it exhibits sub-gaussian properties. This can be done by using a preprocessing.TargetStandardScaler.

Parameters¶

delta

Type → float

The confidence level. Setting this to 1 leads to what is called the UCB1 policy.
reward_obj

Default → None

The reward object used to measure the performance of each arm. This can be a metric, a statistic, or a distribution.
burn_in

Default → 0

The number of steps to use for the burn-in phase. Each arm is given the chance to be pulled during the burn-in phase. This is useful to mitigate selection bias.

Attributes¶

ranking

Return the list of arms in descending order of performance.

Examples¶

import gym
from river import bandit
from river import stats

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

policy = bandit.UCB(delta=100)

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

metric

Sum: 726.

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 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]'

update

Update an arm's state.

Parameters

arm_id
reward_args
reward_kwargs