Multi-agent reinforcement learning algorithms: MADDPG-SFs & MADDPG-SFKT
This is an open-source code for our research work titled "Efficient Exploration for Multi-agent Reinforcement Learning via Transferable Successor Features".
- Python 3.6
- Tensorflow 1.15 + Gym
As introduced in the paper, some source models with basic tasks should be pre-trained in source domain.
- Train:
$ python main_sfs.py --iftrain 1 --scenario "simple_push_box_multi" --method MADDPG-SFs --id-task 0
- Test:
$ python main_sfs.py --iftrain 0 --scenario "simple_push_box_multi" --id-task 0"
is-task: -1, 0, 1, 2
-
Train: Knowledge transfer and fine tune.
"python main_transfer.py --iftrain 0 --istransfer 1 --scenario "simple_push_box_multi" --penalty -1.0"
"python main_transfer.py --iftrain 1 --istransfer 0 --scenario "simple_push_box_multi" --penalty -1.0"
-
Test:
"python main_transfer.py --iftrain 0 --istransfer 0 --scenario "simple_push_box_multi""
As introduced in the paper, some source models with basic tasks should be pre-trained in source domain.
-
Train:
"python main_sfs.py --iftrain 1 --scenario "predator_prey" --method MADDPG-SFs --penalty1 -0.0 --penalty2 -0.0 --prey-policy random"
-
Test:
"python main_sfs.py --iftrain 1 --scenario "predator_prey" --method MADDPG-SFs --penalty1 -0.0 --penalty2 -0.0 --prey-policy random"
-
Train: Knowledge transfer and fine tune.
"python main_transfer.py --iftrain 0 --iftransfer 1 --scenario "predator_prey" --method MADDPG-SFKT --penalty1 -1.0 --penalty2 -1.0 --prey-policy random"
"python main_transfer.py --iftrain 1 iftransfer 0 -scenario "predator_prey" --method MADDPG-SFKT --penalty1 -1.0 --penalty2 -1.0 --prey-policy random"
-
Test:
"python main_transfer.py --iftrain 0 iftransfer 0 -scenario "predator_prey" --method MADDPG-SFKT --prey-policy random"
Paper citation:
@article{liu2022efficient,
title={Efficient exploration for multi-agent reinforcement learning via transferable successor features},
author={Liu, Wenzhang and Dong, Lu and Niu, Dan and Sun, Changyin},
journal={IEEE/CAA Journal of Automatica Sinica},
volume={9},
number={9},
pages={1673--1686},
year={2022},
publisher={IEEE}
}