simulation

Simulation

This tutorial shows how to simulate a TAC.

Tutorial

• Before getting started, check that:

  • You have followed the steps under 'Dependencies' and 'Preliminaries' on root readme.
  • You are connected to the internet (to pull the latest docker images).

Quickstart

Simply run:

  python scripts/launch_alt.py

Manual

• First, ensure that you are running an OEF Node on localhost, using this command (make sure all docker containers are stopped docker stop $(docker ps -q)):

  python scripts/oef/launch.py -c ./scripts/oef/launch_config.json
  

• Second, (in a new terminal window, from root and in shell) start a visdom server:

  python -m visdom.server
  

• Third, (in a new terminal window, from root and in shell) run the simulation example with the dashboard flag to visualize data in realtime:

  python simulation/v1/tac_agent_spawner.py --dashboard
  

• Finally, lean back and watch the competition on http://localhost:8097 in your browser (you might have to select the right environment tac && tac_controller and deselect main in the visdom browser tab).

Optional parameters/flags

For a full list, do python simulation/tac_agent_spawner.py -h

• --nb-agents is the number of agents to participate in the competition.
• --nb-goods is the number of goods in the competition.
• --nb-baseline-agents is the number of number of baseline agents to participate in the competition.
• --oef-addr and --oef-port allow you to specify a different OEF Node to use for the simulation.
• --register-as the string indicates whether the baseline agent registers as seller, buyer or both on the oef.
• --search-for the string indicates whether the baseline agent searches for sellers, buyers or both on the oef.
• --uml specifies whether or not to store the activity of the simulation in PlantUML syntax.
• --data-output-dir is the output directory to use for storing simulation data in ${data_output_dir}/${experiment_id}.
• --version-id is the name to give to the simulation.
• --plot specifies whether to plot a summary of the game.
• --money-endowment is the money amount every agent receives.
• --base-good-endowment is the base amount of per good instances every agent receives.
• --lower-bound-factor is the lower bound factor of a uniform distribution used for generating good instances.
• --upper-bound-factor is the upper bound factor of a uniform distribution used for generating good instances.
• --tx-fee is the transaction fee.
• --registration-timeout is the amount of time (in seconds) to wait for agents to register before attempting to start the competition.
• --inactivity-timeout is the amount of time (in seconds) to wait during inactivity until the termination of the competition.
• --competition-timeout is the amount of time (in seconds) to wait from the start of the competition until the termination of the competition.
• --visdom-addr is the TCP/IP address of the Visdom server
• --visdom-port is the TCP/IP port of the Visdom server
• --dashboard is a flag to specify that the dashboard is live and expecting an event stream.
• --seed is the seed for the random module.
• --fraction-world-modeling the fraction of world modelling baseline agents.

Example:

  python simulation/v1/tac_agent_spawner.py 
      --nb-agents 10
      --nb-goods 10
      --nb-baseline-agents 10
      --oef-addr oef.economicagents.com 
      --oef-port 10000
      --service-registration-strategy both
      --uml True
      --data-output-dir data
      --version-id my_experiment
      --plot True
      --money-endowment 200
      --base-good-endowment 2
      --lower-bound-factor 1
      --upper-bound-factor 1
      --tx-fee 1
      --registration-timeout 10
      --inactivity-timeout 60
      --competition-timeout 240
      --dashboard
      --seed 42

It generates a game.json file in the ${data_output_dir}/${version_id} that can be inspected with a dashboard (see tac/gui).

Scalability

We can confirm that the simulation runs successfully on a MacBook Pro (13-inch, 2017, 3.1 GHz Intel Core i5, 16 GB 2133 MHz LPDDR3) with 60 agents and 10 goods. Higher number of agents cause issues with threading.