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))
A distribution to sample from.
burn_in – defaults to
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 (int) – defaults to
Random number generator seed for reproducibility.
Return the list of arms in descending order of performance.
>>> 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(dist=proba.Beta(), seed=101) >>> 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: 820.
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
- arm_ids (List[Union[int, str]])
Update an arm's state.