Exp3¶

Exp3 bandit policy.

This policy works by maintaining a weight for each arm. These weights are used to randomly decide which arm to pull. The weights are increased or decreased, depending on the reward. An egalitarianism factor \(\gamma \in [0, 1]\) is included, to tune the desire to pick an arm uniformly at random. That is, if \(\gamma = 1\), the arms are picked uniformly at random.

Parameters¶

gamma

Type → float

The egalitarianism factor. Setting this to 0 leads to what is called the EXP3 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.
reward_scaler

Default → None

A reward scaler used to scale the rewards before they are fed to the reward object. This can be useful to scale the rewards to a (0, 1) range for instance.
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.
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 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.Exp3(gamma=0.5, seed=42)

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: 799.

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