Ethical harvest

Demo Video:

Based on the Harvest environment from: Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., & Graepel, T. (2017). Multi-agent reinforcement learning in sequential social dilemmas. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (pp. 464-473).

This is the code for the final assignment of the Self-organizing Multiagent Systems course at the University of Barcelona.

How to run the code:

• install python with pip (tested for Python 3.8.5 )

• install the required libs via pip install -r requirements.txt

• install the dependencies required by tensorflow, see: https://www.tensorflow.org/install/gpu#software_requirements

• For the training run python Learning.py from the desired working directory.

• You can set the name of the training run in the first set_config call in Learning.py. This is the name of the subfolder in the working directory to which all relevant data for the training run will be saved.

• To open the plots in the browser:

  • Either change to ./impl/vis and run python vis.py. This allows to select and view graphs for any of the experiments saved in the working directory.
  • Or edit ./Learning.py and change SERVE_VISUALIZATION to True. This shows graphs only for the running training session but also updates as new data becomes available.

• To evaluate and agent:

  • edit Learning.py and set the EVALUATE_EXPERIMENT variable to the name of the folder in the working directory which contains the data from the experiment for which you want to evaluate an agent.
  • also set the EPISODE_NUMBER variable to the episode number for which you want the agents to load their respective model weights. You can have a look to the folder ./<experiment_name>/weights/ in your chosen working directory to see for which previous training episodes model weights have been dumped.
  • To load the trace file from the evaluation run rename the file trace_eval.txt in the respective subfolder of the working directory to trace.txt and open the visualization tool as described above.

For more information please read the corresponding report.