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PyLoa: Learning on-line Algorithms with Python

Project description

PyLoa - Learning on-line Algorithms with Python

pyloa is a research repository for analyzing the performance of classic on-line algorithms vs. modern Machine Learning, specifically Reinforcement Learning, approaches. PyLoa ships with an implementation of two commonly known on-line problems as environments:

  • (k,n)-paging-problem with a cache_size k and n pages for a sequence of page-requests
  • (k,n)-coloring-problem with k colors for a graph with n vertices

PyLoa allows for agents to be

  • trained on such enviroments (problem definitions) that require on-line solutions,
  • evaluated against commonly used heuristics or any state-of-the-art algorithm,
  • exploited (extrapolation of a potentially worst case problem instances) to determine a solution's competitve ratio.


pyloa is developed for Python 3.5+ and has the following package dependencies:



We recommend using pyloa within a virtual environment:

mkdir myproject
cd myproject
python3 -m venv virtualenv/
source virtualenv/bin/activate

Update pip and setuptools before continuing:

pip install --upgrade pip setuptools

Afterwards you can install pyloa either from its latest PyPI stable release

pip install pyloa

or from its latest development release on GitHub

pip install git+

General Usage

pyloa can be used in three different ways to analyze an on-line problem; each depicted via a so called runmode (train, eval, gen). Any runemode can be invoked via its positional argument and requires a python-configuration-file.

pyloa {train,gen,eval} --config path/to/

hyperparams depicts the setting of the experiment at hand; it must hold a dictionary named params, which moreover must contain dictionaries for the keys instance, environment and agent.

  • params["ìnstance"]: Must define a configuration of a subclass implementation of pyloa.instance.InstanceGenerator, which generates problem instances for the domain. As an example, for the (k,n)-paging-problem a simple generator could randomly generate a sequence of requests of length sequence_size, whereas each request is within [1, n].
  • params["agent"]: Must define a configuration of a subclass implementation of pyloa.agent.Agent, which observes a state s of its environment, acts with action a accordingly, receives reward r and observes transitioned state s'. For toy problem instances a simple Q-learning table implementation would suffice.
  • params["environment"]: Must define a configuration of a subclass implementation of pyloa.environment.Environment, which consumes a problem instance and let's the agent play until it terminates. An environment constitutes as a problem definition.

A minimal example for learning the (5,6)-paging-problem with a QTableAgent on a PagingEnvironment can be invoked with

pyloa train --config

and the as following:

from pyloa.instance import RandomSequenceGenerator
from pyloa.environment import DefaultPagingEnvironment
from pyloa.agent import QTableAgent

# vars
sequence_size = 1000
max_page = 6
min_page = 1
episodes = 250

# hyperparams
params = {
    'checkpoint_step': episodes//10,
    'instance': {
        'type': RandomSequenceGenerator,
        'sequence_size': sequence_size,
        'sequence_number': episodes,
        'min_page': min_page,
        'max_page': max_page,
    'environment': {
        'type': DefaultPagingEnvironment,
        'sequence_size': sequence_size,
        'cache_size': 5,
        'num_pages': max_page - min_page + 1,
    'agent': {
        'type': QTableAgent,
        'discount_factor': 0.55,
        'learning_rate': 0.001,
        'epsilon': 0.0,
        'epsilon_delta': 13 / (episodes * 10),
        'epsilon_max': 0.99,
        'save_file': "/home/me/models/",

This example is defined in examples/0_train_qtable_paging/ and can be run with

pyloa train --config examples/0_train_qtable_paging/

The resulting run can be seen here. In total there are five toy examples, which can be run on any system, defined in the examples directory.


PyLoa has three different runmodes: train ,eval and gen. There are slight adaptions to be made for the configuration file depending on the selected runmode; we encourage checking the examples for reference (on a site note: hyperparams are loaded and validated in pyloa.utils.load). Semantically the three different runmodes stand for:

  • train: An RLAgent will be trained for episode-many instances, generated by an InstanceGenerator, on his environment. Every checkpoint_step-many instances a checkpoint of RLAgent will be saved.
  • eval: All trained RLagents nested within root_dir will be evaluated on episode-many instances, generated by an InstanceGenerator. Additionally non-trainable agents may be defined and evaluated alongside.
  • gen: Currently only applicable for the (k,n)-paging-problem. A genetic algorithm empirically determines a PagingAgent's (approximate) competitive ratio.

Each runmode will create TFEvent-files for TensorBoard in its experiment's output directory.

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