Configuration

Module settings

After enabling the module, visit Administration > Configuration > Web services > RL: A/B Testing (/admin/config/services/reinforcement-learning) to configure the module.

You can also manage settings via Drush:

# List all settings with current values
drush rl:config:list

# Get a specific setting
drush rl:config:get debug_mode

# Set a specific setting
drush rl:config:set event_log_max_rows 50000

See the Drush commands guide for the full reference.

Choosing a variant selection strategy

RL supports two approaches for deciding which variant to show. Pick the one that fits your caching setup.

Server-side (preferred)

Pick the winning variant in PHP at render time whenever you can. Your consumer already has the full arm set in hand (entity fields, view filters, plugin config), so it can call the RL service directly:

$scores = $experiment_manager->getThompsonScores(
  'my_experiment',
  NULL,
  ['v0', 'v1', 'v2'],
);
arsort($scores);
$best_arm = key($scores);

Deciding server-side keeps the arm list where it belongs (with the experiment owner) and avoids a network round trip on every page load. See rl_sorting's Views sort plugin for the canonical pattern and VariantSelectorBase in this module for a reusable base class. The Building a Consumer Module guide walks through both bundled submodules as worked examples.

Client-side (cache-friendly path)

Some consumers have to decide in JS: full-page-cached builders that render all variants into the HTML and swap them on the client so they can keep Varnish/Fastly caching. For that case there is Drupal.rl.decide(), documented in the JavaScript API.

Viewing reports

Once experiments are running and collecting data, visit Administration > Reports > RL (/admin/reports/rl) to see per-experiment performance, traffic distribution, and confidence levels.

Click View on any experiment to see its detail page with interactive charts. The 2D line chart shows conversion scores over time; the 3D posterior landscape visualises how Thompson Sampling distributions evolve across variants and impressions.