# Bayesian¶

## Using Bayesian Method on a Bernoulli Multi-Armed Bandit¶

For an introduction to Multi Armed Bandits, refer to Multi Armed Bandit Overview

This method is also based on the prinicple - ‘Optimism in the face of uncertainty’, like UCB. We initially assume an initial distribution(prior) over the quality of each of the arms. We can model this prior using a Beta distribution, parametrised by alpha($$\alpha$$) and beta($$\beta$$).

$PDF = \frac{x^{\alpha - 1} (1-x)^{\beta -1}}{B(\alpha, \beta)}$

Let’s just think of the denominator as some normalising constant, and focus on the numerator for now. We initialise $$\alpha$$ = $$\beta$$ = 1. This will result in a uniform distribution over the values (0, 1), making all the values of quality from 0 to 1 equally probable, so this is a fair initial assumption. Now think of $$\alpha$$ as the number of times we get the reward ‘1’ and $$\beta$$ as the number of times we get ‘0’, for a particular arm. As our agent interacts with the environment and gets a reward for pulling any arm, we will update our prior for that arm using Bayes Theorem. What this does is that it gives a posterior distribution over the quality, according to the rewards we have seen so far.

This is quite similar to Thompson Sampling. But what is different here is that we explicity try to calculate the uncertainty of a particular action by calculating the standard deviation($$\sigma$$) of its posterior. We add this std. dev to the mean of the posterior, giving us an upper bound of the quality of that arm. At each timestep we select a greedy action based on this upper bound we calculated.

$a_t = argmax(q_t(a) + \sigma_{q_t})$

As we try out an action more and more, the standard deviation of the posterior decreases, corresponding to a decrease in the uncertainty of that action, which is exactly what we want. If an action has not been tried that often, it will have a wider posterior, meaning higher chances of it getting selected based on its upper bound.

Code to use Bayesian method on a Bernoulli Multi-Armed Bandit:

import gym
import numpy as np

from genrl.bandit import BayesianUCBMABAgent, BernoulliMAB, MABTrainer

bandits = 10
arms = 5
alpha = 1.0
beta = 1.0

reward_probs = np.random.random(size=(bandits, arms))
bandit = BernoulliMAB(bandits, arms, reward_probs, context_type="int")
agent = BayesianUCBMABAgent(bandit, alpha, beta)

trainer = MABTrainer(agent, bandit)
trainer.train(timesteps=10000)


More details can be found in the docs for BernoulliMAB, BayesianUCBMABAgent and MABTrainer.