ThompsonSampling

Thompson sampling.

Thompson sampling is often used with a Beta distribution. However, any probability distribution can be used, as long it makes sense with the reward shape. For instance, a Beta distribution is meant to be used with binary rewards, while a Gaussian distribution is meant to be used with continuous rewards.

The randomness of a distribution is controlled by its seed. The seed should not set within the distribution, but should rather be defined in the policy parametrization. In other words, you should do this:

policy = ThompsonSampling(dist=proba.Beta(1, 1), seed=42) 

and not this:

policy = ThompsonSampling(dist=proba.Beta(1, 1, seed=42)) 

Parameters

• reward_obj

  Type → proba.base.Distribution

  Default → None

  A distribution to sample from.

• 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.ThompsonSampling(reward_obj=proba.Beta(), seed=101)

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

metric

Sum: 820.

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

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1. An Empirical Evaluation of Thompson Sampling ↩