Skip to content

hanskasan/IE579_MARL

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

14 Commits
 
 
 
 
 
 

Repository files navigation

IE579_MARL (Last updated (grading policy) at Nov 26, 11:25 am)

An tutorial code for the Homework #4 in KAIST IE579 class.

Code structure

DGN-R.ipynb: contains tutorial code for implementing GRAPH CONVOLUTIONAL REINFORCEMENT LEARNING (ICLR, 2020)

paper link: https://arxiv.org/pdf/1810.09202.pdf

simple_spread.py: contains a modified version of simple_spread.py in multiagent-particle-envs package.

original code: https://github.com/openai/multiagent-particle-envs/blob/master/multiagent/scenarios/simple_spread.py

Requirements

Multi-Agent Particle Environment package (from https://github.com/openai/multiagent-particle-envs)

pip install git+https://github.com/openai/multiagent-particle-envs

pip install gym==0.10.5 # to downgrade gym

Deliverable (score: 50)

  • Code with consistent regularization term (.ipynb or .py)
  • Figure with results
    • should include loss, reward of two algorithms (w/, w/o consistent regulization)

Grading policy

  • Just adding KL regulation would be enough.
  • It would be best if you achieve good performance, but it is difficult to achieve good results since this environment is originally designed more suitable for continuous action space than discrete action space.
  • So I will not include the performance to the score of this homework.

Contact

[email protected]

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.5%
  • Python 0.5%