MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents

This repository is the official implementation of the paper "MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents". (https://arxiv.org/abs/2010.07893)

Requirements

Only gym and some basic packages are required to run the code. To install the requirements, run:

pip install -r requirements.txt

Training

To train the model on the multiplexer task, run this command:

python main_pre.py -c config_mp.ini

To train the model on the scalar regression task, run this command:

python main_pre.py -c config_sr.ini

To train the model on the Acrobot task, run this command:

python main.py -c config_ab.ini

To train the model on the CartPole task, run this command:

python main.py -c config_cp.ini

To train the model on the LunarLander task, run this command:

python main.py -c config_ll.ini

To train the model on the MountainCar task, run this command:

python main.py -c config_mc.ini

This will load the config file in config folder to run the experiment. By default, 10 runs of training will be done. The result will be stored in the result folder and the learning curve will be shown. You can edit the config file to adjust hyperparameters.

Results

Our model has the following result on the four RL tasks. See paper for details of the result.

	Acrobot	CartPole	LunarLander	MountainCar
Average return over all episodes - Mean (Std.)	-100.29 (5.40)	459.70 (13.89)	127.88 (24.57)	39.45 (30.48)

Contributing

This software is licensed under the Apache License, version 2 ("ALv2").