src

Installing and Running Novelty Generator

This package provides a sample TA2 client, configuation file, and auxiliary files for interacting with the WSU SAIL-ON Novelty Generator (NG). The client connects to the NG server and calls various methods in the TA2Agent class.

The SAIL-ON NG is a variant of the WSU AIQ testing facility, so some references to AIQ appear.

Contact Larry Holder ([email protected]) for more information.

Table of Contents

• Installation
• Configuration File
• Running the Client
• Running Modes
• Program Flow
• TA2 Agent

Installation

The package is available here in the src directory. The code requires some Python setup, which we describe below based on the Anaconda environment.

1. Install Anaconda.

2. Activate the base Anaconda environment.

[user@host ~]$ source ~/anaconda3/bin/activate

3. Ensure your base environment is up to date:

(base) [user@host ~]$ conda update -n base -c defaults conda

4. Create a new environment with the basic versions.

(base) [user@host ~]$ conda create --name aiq-env python=3.7 python-dateutil==2.8.1 psutil pytz numpy

5. Activate the new environment so we can finish installing the remaining packages.

(base) [user@host ~]$ conda activate aiq-env

6. Install the remaining packages with pip.

(aiq-env) [user@host ~]$ pip install pika==1.1.0 blosc==1.10.4

Configuration File

The configuration file has three sections: aiq-sail-on, sail-on, and amqp. See demo-client.config for an example. This demo config works now, but only provides a small set of novelty level 0 data. To run an actual experiment with all novelty levels, contact us for different credentials.

[aiq-sail-on]

• experiment_type selects the type of experiment to run, SAIL-ON is the only valid experiment type currently supported.

• organization is an identifier for your organization or university, this is associated with the experiments you run.

• model_name is the identifier for your model. This allows you to keep track of multiple models.

• username is the email address/login for the system website (under construction).

• secret is a secret separate from your website password that is provided to allow for authentication. Once the website is complete you will be able to request new values for this if you compromise the previous value.

• description is an optional field that will be recorded and associated with the experiment instance that is run.

• seed is an optional integer that will provide the random seed used when building an experiment so that you can always build the same experiment.

• episode_seed is an optional integer that will overwrite the experiment setting and force EVERY episode to use this seed value.

• start_zeroed_out is an optional boolean (default=False) for the CartPole domain that will have your cart physics start zeroed out when set to True.

• start_world_state is an optional JSON string that is converted to a dictionary representing the starting world state for the CartPole domain. The string is converted using json.loads(val) and will throw an exception if the string is not a valid dictionary.

[sail-on]

• domain selects which domain you would like to test on. The current available domains are cartpole, vizdoom and smartenv.

• experiment_secret is a secret generated when a new experiment is created. This field is optional if you just wish to run the system in a single linear experiment, but is required if you wish to run additional TA2 agents to help process trials. The TA2 agent automatically updates the config file with this value once the experiment is created. Please see the section Running Modes for information on how this entry interacts with no_testing and just_one_trial for TA2 agent behavior. Passing in the command line argument --ignore-secret will have the TA2 agent behave as if experiment_secret is not defined.

• no_testing is an optional boolean (default=False) that is used for informing the TA1 that this TA2 does not wish to begin the testing phase of the experiment, instead it will cleanly exit after creating the experiment, saving the experiment_secret in the config file, processing any training episodes, and optionally training the model if needed by your domain. Please see the section Running Modes for information on how this entry interacts with no_testing and just_one_trial for TA2 agent behavior. The config file value can be overridden to True by passing --no-testing as a command line argument.

• just_one_trial is an optional boolean (default=False) that is used for informing the TA1 that this TA2 should process one trial for the given experiment_secret. If the entry for experiment_secret is not defined, setting just_one_trial = True will result in an exception being raised and the program exiting. Please see the section Running Modes for information on how this entry interacts with no_testing and just_one_trial for TA2 agent behavior. The config file value can be overridden to True by passing --just-one-trial as a command line argument.

[amqp]

• user is the username for authenticating to our RabbitMQ server.

• pass is the password for authenticating to our RabbitMQ server.

• host is the hostname for our RabbitMQ server, aiq.ailab.wsu.edu.

• vhost is the optional vhost to connect to on our RabbitMQ server.

• port is the port on our RabbitMQ server to connect to, 5671.

• ssl is a boolean identifying if the client will use the SSL connection.

Running the Client

To run the client on the provided demo-client.config configuration file, do the following.

1. Ensure that you are in the conda environment initialized above (e.g., aiq-env).
2. Run the TA2 client in default mode.

(aiq-env) [user@host client]$ python TA2.py --config=demo-client.config --printout --debug --logfile=log.txt

All command line arguments are described with python TA2.py --help.

Running Modes

There are 4 differnt modes the TA2 can run in, here are the variations using the new config values:

experiment_secret	no_testing	just_one_trial	Behavior
Not Defined	False	True	Exception Thrown
Not Defined	False	False	Mode #1 - Full Linear Experiment
*	True	*	Mode #2 - No Testing
Defined	False	True	Mode #3 - Just One Trial
Defined	False	False	Mode #4 - Trials Until Done

For Mode #3 and Mode #4, if the experiment is complete you will receive an error message that the experiment is already complete before cleanly exiting. None of the functions (other than __init__()) in TA2.py will be called when this happens, a function can be added requested.

Mode #1 - Full Linear Experiment

Full linear experiment runs the full experiment in linear fashion.

• Create experiment in database.
• Iterate through training episodes.
• Train model.
• Iterate through experiment trials.

Example Use

This assumes experiment_secret is either not defined in the config file or you add the --ignore-secret flag to the command line.

(aiq-env) [user@host client]$ python TA2.py --config=demo-client.config --printout

Mode #2 - No Testing

No testing informs the TA1 that the TA2 does not with to iterate through the trials on this connection. This is intended for use in creating the experiment before starting multiple TA2 instances running in Modes #3 or #4.

• Create experiment in database.
• Iterate through training episodes.
• Train model.

Example Use

Here we initially run in Mode #2 to go through any training data, if needed, and training of the model.

(aiq-env) [user@host client]$ python TA2.py --config=demo-client.config --printout --no-testing

After this runs the experiment_secret has been updated in the config file, and we can now use this config (and a trained model) to run in Mode #3 or Mode #4, with just one instance or many at the same time.

Mode #3 - Just One Trial

Just one trial informs the TA1 that the TA2 only wants to process a single trial from the defined experiment_secret and then cleanly exit. This is intended for running multiple versions of TA2 on a cluster using a job queue with a limited runtime.

• Process a single experiment trial.

Example Use

This requires that experiment_secret is set in the config file, if not it will throw an exception.

(aiq-env) [user@host client]$ python TA2.py --config=demo-client.config --printout --just-one-trial

Mode #4 - Trials Until Done

Trials until done informs the TA1 that the TA2 would like to process trials from the defined experiment_secret until there are no more trials available for the given experiment.

In order to deal with the potential of a TA2 crashing or disconnecting before completing a trial, the TA1 will delete the work for a trial and make it available for the next TA2 requesting work if there has been no update to the progress of a given trial in an hour. An experiment is only marked complete when all trials in that experiment are marked as complete. There is currently no method for providing feedback on if an experiment is complete or if there are no more trials currently available to process, this may be considered in a future version if requested.

• Iterate through experiment trials.

Example Use

This requires that experiment_secret is set in the config file, if not it will actually run in Mode #1 and create a new experiment.

(aiq-env) [user@host client]$ python TA2.py --config=demo-client.config --printout

Program Flow

Running the client results in the following program flow. As the client enters different phases of the experiment, the corresponding method in the TA2Agent class is called.

• The client connects to the RabbitMQ server and requests to start an experiment.

• The server requests benchmarking information and waits for the results. Currently, this is just hardware information from the client, but eventually will be a benchmarking script.

• The experiment starts!

• Training begins.

• For episode in training episodes:

  • For feature vector in episode:

    • Train on feature vector and return prediction.

• Training is over, you may optionally train your model here.

• TA2 should save the current state of the model so you can revert back to this state at the start of each trial.

• For novelty in novelty levels:

  • For difficulty in difficulty levels:

    • For novelty_visibility in [no visibility, novelty visible]:

      • Testing begins.

      • For trial in number of trials:

        • TA2 should reset the model to the saved state at this point.

        • For episode in testing episodes:

          • For feature vector in episode:

            • Evaluate feature vector and return your prediction.

      • Testing ends.

• The experiment concludes!

• Analysis scripts will run with results being made available on the website. This is currently under construction and we will be emailing results while the website is completed.

TA2 Agent

The sample TA2 client in TA2.py provides stubs for the methods that are called for each of the different phases of the program flow above. This is where you implement your TA2/AI agent. See the documentation comments on these methods in the TA2.py file.