mab-algo 1.0.0

Implementation of Multi Armed Bandit algorithms from "Reinforcement Learning - An Introduction" by Richard S. Sutton and Andrew G. Barto

Multi-Armed Bandits

Implementation of Multi Armed Bandit algorithms from "Reinforcement Learning - An Introduction" by Richard S. Sutton and Andrew G. Barto

This library suggests the next actions based on the rewards received for each action.

Algorithms

This library covers the following algorithms:

• Epsilon Greedy
• Upper Confidence Bound
• Gradient Bandit

Epsilon Greedy

This algorithm selects an action based on an epsilon variable.

Probability	Action
1 - epsilon	action with highest estimated reward
epsilon	random action

Note 0 <=epsilon<= 1

Upper Confidence Bound

This algorithm selects the next action based on the confidence in the estimated values.

This uses an exploration parameter to control the balance between exploration and exploitation.

A larger exploration value means the algorithm explores more.

Note exploration > 0

Gradient Bandit Algorithm

This algorithm selects an action based on a preference.

Internally it uses a soft-max distribution to convert preferences to probabilities.

It has a step_size parameter, which influences how much preferences are changed at each step. A larger step_size will learn faster from the most recent reward.

Note step_size > 0

Averaging Functions

The algorithms could be used with any averaging function:

• Simple Average (a.k.a running average)
• Weighted Average
• Exponential Moving Average

Simple Average

A simple average is the same as a running average. As time steps are advanced, the average is adjusted to account for the reward.

A higher reward, increases the average and a lower reward decreases the average.

Weighted Average

A weighted average operates much like a running average but it applies a step_size parameter. The step_size parameter controls how much weighting is given to new rewards.

A higher step_size gives more weighting to the most recent reward.

Note step_size > 0

Exponential Moving Average

An exponential moving average applies a discount multiplier to previous rewards and (1 - discount) multiplier to recent rewards.

A higher discount means more weight is given to previous average, making recent rewards less influential.

A lower discount means more weight is given to the most recent reward, making older rewards less influential.

Usage

To use this algorithm first import this library:

from mab_algo import *

Declare a set of actions:

actions = ["A", "B", "C"]

Select any averaging function:

averager = SimpleAverage()
averager = WeightedAverage(step_size = 0.2)
averager = ExponentialMovingAverage(discount = 0.8)

Note This uses example step_size and discount values, please refer to the sections above to learn more about them.

Select any algorithm:

algo = EpsilonGreedy(
    actions = actions,
    averager = averager,
    epsilon = 0.5
)

algo = UCB(
    actions = actions,
    averager = averager,
    exploration = 0.35
)

algo = GradientBandit(
    actions = actions,
    averager = averager, 
    step_size = 0.9
)

Note This uses example epsilon, exploration and step_size values, please refer to the sections above to learn more about them.

Step through the algorithms to get the next actions:

action = algo.step() 
action = algo.step(reward = 0.5)
action = algo.step(reward = 5)
# ... continue as needed

Verbose

action = algo.step() # get initial action
reward = 0.5 # for taking 'action' - reward must be supplied by you 
next_action = algo.step(reward)
reward = 5 # for taking 'next_action' - reward must be supplied by you
next_action = algo.step(reward)
# ... continue stepping through and providing reward values to get new actions...

example.py file contains an example of the above in action!

Testing

Unit tests are provided in the 'tests' directory.

Tests can be executed using this command:

python3 -m unittest discover tests

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