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Project 3: RoboCup

The repo contains code to run experiments required for CS 7642 Summer 2018 Project 3.

  • Required packages: Python 3.6.5, Numpy 1.15.5, Pandas 0.23.1, Matplotlib 2.2.2, cvxopt 1.2.0, progress 1.4.

    • progress 1.4 can be found here, and source code here.
  • The repo contains these folders:

    • results: contains csv files that are results from each experiments, Q tables in the format of .npy, Q-value difference plots, as well as log.csv recording parameters used in all experiments. This folder needs to be created before any experiment can be run.
    • figs: plots used in figures in the report.
    • report: full report in PDF and LaTex format, and accessory files
  • The repo contains these files:

    • README.md: this file, description of this repo
    • Q.py: Q learner, with different variants, called from run.py. All learner classes includes both learn() and train() methods.
    • friendQ.py: CE-Q learner, with different variants.
    • foeQ.py: foe-Q learner, with different variants.
    • ceQ.py: CE-Q learner, with different variants.
    • run.py: takes parameters as argument and run experiments
    • run.sh: bash script to run experiments with different parameters in batch.
    • util.py: utility functions, including plotting and logging functions, and linear programming functions for maximin and CE.
    • soccer.py: soccer game environment.
  • Perform experiments by running python3 run.py <algo_name> <init_lr> <end_lr> <init_eps> <end_eps> <max_iter> from terminal, or running bash run.sh to perform experiments in batch.

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Multi-agent Q learning for a small game

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