CORE-Robotics-Lab/InfoPG

Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming

InfoPG: Mutual Information Maximizing Policy Gradient

This repo provides the full implementation for the paper "Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming" at the International Conference on Learning Representations (ICLR) 2022

Authors: Sachin Konan*, Esmaeil Seraj*, Matthew Gombolay

* Co-first authors. These authors contributed equally to this work.

Full Read (arXiv): https://arxiv.org/pdf/2201.08484.pdf

Installation Instructions:

1. Download Anaconda
2. conda env create --file marl.yml
3. cd PettingZoo
4. conda activate marl
5. python setup.py install
6. Follow Starcraft MultiAgent Challenge Instructions Here: https://github.com/oxwhirl/smac

Run PistonBall:

1. cd pistonball
2. To Execute Experiments:
   1. MOA: python test_piston_ball.py -method moa
   2. InfoPG: python test_piston_ball.py -method infopg -k [K_LEVELS]
   3. Adv. InfoPG: python test_piston_ball.py -method infopg_adv -k [K_LEVELS]
   4. Consensus Update: python test_piston_ball.py -method consensus
   5. Standard A2C: python test_piston_ball.py -method a2c
3. To Execute PR2-AC Experiments:
   1. cd ../pr2-ac/pistonball/
   2. python distributed_pistonabll_train.py -batch 4 -workers [NUM CPUS]
   3. Results will be saved in experiments/pistonball/[DATETIME OF RUN]/

Run Fraud (Byzantine Experiments):

1. MOA: python batch_pistoncase_moa_env.py
2. InfoPG: python batch_pistoncase_infopg_env.py

Run Pong:

1. cd pong
2. To Execute MOA Experiments:
   1. cd pong_moa
   2. MOA: python distributed_pong_moa_train.py -batch 16 -workers [NUM CPUS]
   3. Results will be saved in experiments/pong/[DATETIME OF RUN]/
3. To Execute PR2-AC Experiments:
   1. cd ../pr2-ac/pong/
   2. python distributed_pong_train.py -batch 16 -workers [NUM CPUS]
   3. Results will be saved in experiments/pong/[DATETIME OF RUN]/
4. To Execute Other Experiments:
   1. InfoPG: python distributed_pong_train.py -batch 16 -workers [NUM CPUS] -k [K_LEVELS] -adv info -critic
   2. Adv. InfoPG: python distributed_pong_train.py -batch 16 -workers [NUM CPUS] -k [K_LEVELS] -adv normal
   3. Consensus Update: python distributed_pong_train.py -batch 16 -workers [NUM CPUS] -k 0 -adv normal -consensus
   4. Standard A2C: python distributed_pong_train.py -batch 16 -workers [NUM CPUS] -k 0 -adv normal
   5. Results will be saved in experiments/pong/[DATETIME OF RUN]/

Run Walker:

1. cd walker
2. To Execute MOA Experiments:
3. cd walker_moa
4. MOA: python distributed_walker_train_moa.py -batch 16 -workers [NUM CPUS]
5. Results will be saved in experiments/walker_moa/[DATETIME OF RUN]/
6. To Execute PR2-AC Experiments:
   1. cd ../pr2-ac/walker/
   2. python distributed_walker_train.py -batch 16 -workers [NUM CPUS]
   3. Results will be saved in experiments/walker/[DATETIME OF RUN]/
7. To Execute Other Experiments:
   1. InfoPG: python distributed_walker_train.py -batch 16 -workers [NUM CPUS] -k [K_LEVELS] -adv info -critic
   2. Adv. InfoPG: python distributed_walker_train.py -batch 16 -workers [NUM CPUS] -k [K_LEVELS] -adv normal
   3. Consensus Update: python distributed_walker_train.py -batch 16 -workers [NUM CPUS] -k 0 -adv normal -consensus
   4. Standard A2C: python distributed_walker_train.py -batch 16 -workers [NUM CPUS] -k 0 -adv normal
   5. Results will be saved in experiments/walker/[DATETIME OF RUN]/

Run Starcraft:

1. cd starcraft
2. To Execute MOA Experiments:
   1. cd moa
   2. MOA: python distributed_starcraft_train_moa.py -batch 128 -workers [NUM CPUS] -positive_rewards
   3. Results will be saved in experiments/starcraft/[DATETIME OF RUN]/
3. To Execute PR2-AC Experiments:
   1. cd ../pr2-ac/starcraft/
   2. python distributed_starcraft_train.py -batch 128 -workers [NUM CPUS]
   3. Results will be saved in experiments/starcraft/[DATETIME OF RUN]/
4. To Execute Other Experiments:
   1. InfoPG: python distributed_walker_train.py -batch 128 -workers [NUM CPUS] -k [K_LEVELS] -adv info -critic -positive_rewards
   2. Adv. InfoPG: python distributed_walker_train.py -batch 128 -workers [NUM CPUS] -k [K_LEVELS] -adv normal -positive_rewards
   3. Consensus Update: python distributed_walker_train.py -batch 128 -workers [NUM CPUS] -k 0 -adv normal -consensus -positive_rewards
   4. Standard A2C: python distributed_walker_train.py -batch 128 -workers [NUM CPUS] -k 0 -adv normal -positive_rewards
   5. Results will be saved in experiments/starcraft/[DATETIME OF RUN]/