Quickstart¶
This walkthrough follows the in-app Setting up an experiment tour: a Classic RL project, one experiment, default settings, train, then view results. Allow about 15–20 minutes if compute queues quickly.
1. Sign in and open Experiments¶
After login you are on Home. In the left sidebar, click Experiments.
2. Create a project¶
On Experiments, click New project. If you have no projects yet, the empty-state button is Create Project. Either way the dialog is Create Project. Fill in Name and Description. Leave Classic RL selected on the Training switcher.
The app opens the new project page.
3. Create an experiment¶
On a new project, click Create Experiment. If the project already has runs, use New experiment in the toolbar. The dialog is Create Experiment. Name it and confirm. The setup wizard opens: Resources, Environment, Agent, Training, HPO, Summary.
4. Resources¶
The heading is Training cluster. Choose Number of nodes, GPU class, GPU size, and Number of GPUs. If BYOC is set up, Compute appears first (Arena or your cluster). Prices are per hour across all nodes. Click Next. You can change this later with Edit resources, which opens Select training cluster.
Your remaining balance appears in the profile menu and on Usage for org Admins.
5. Environment¶
Arena ships with many built-in Gym environments. Pick any environment for this walkthrough. Validation summary, rollout reward charts, and an optional render appear on the same step when data exists. Click Next to go to Agent.
For your own code, use Environments in the sidebar and the custom-environment tutorial instead.
6. Agent (algorithm)¶
Choose an algorithm (the tutorial uses PPO). You can edit network and algorithm hyperparameters; the defaults are enough here. Next.
7. Training and HPO¶
On Training, leave step counts and related settings as they are. Next.
On HPO (hyperparameter optimization), evolutionary search is optional. Leave the defaults and Next. See How evolutionary hyperparameter optimization works if you want the full picture before tuning mutation settings.
8. Train¶
On Summary, review the configuration and click Train. The experiment appears in the project’s Experiments table with a live status.
Training debits the balance while the job runs. On pay as you go, new runs stop when the balance is $10.00 or lower until an Admin adds balance (see Balance and plans).
9. Results¶
Open the Results tab for the project (the tutorial highlights this tab at the end). Select your experiment to inspect metrics, logs, and checkpoints when the run finishes.
Statuses such as Running, Succeeded, and Failed are defined in Experiment statuses.
Optional: custom environment tutorial¶
From the sidebar, open Environments and follow the Upload a custom environment tutorial: upload code, pick an entrypoint, validate, save, then launch an experiment from the environment page.
CLI¶
To run jobs from a terminal instead of the UI, create a CLI API key on Profile management and configure the Arena Python CLI. See Arena CLI.