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How does this work again?
Multiagent RL is a system to execute and evaluate multiple simulated robotic agents and have them learn to select appropriate behaviors in a stochastic environment. Currently, the approach is tested in a predator-prey problem using a modified version of the Pac-Man game with introduced uncertainties. Therefore, this simplified multi-agent situation aims to answer the following question: can the ghosts learn to get the Pac-Man?
In order to run the system, two processes are necessary: one that implements agents intelligence, in our case controller.py
, and another that provides an interface to the real agents. Since we are using the Pacman simulator, we interface it in the module simulator.py
.
First, it's necessary to run the controller.py
script, which will launch a server that listens to simulator messages, process them by learning with new information, and returns actions for each agent.
python controller.py
Next, we can launch the Pac-Man simulation with default settings by invoking the command below.
python simulator.py
The simulation may be customized with several implemented flags. Check all available settings by executing:
python simulator.py -h
If configured to save results into a file, the plot.py
script can be used to visualize simulation results. For instance, the following command plots all graphs for a results.txt
file.
python plot.py -i results.txt