DyPs

This is the code for paper "DyPS: Dynamic Parameter Sharing in Multi-Agent Reinforcement Learning for Spatio-Temporal Resource Allocation "

Installation and Setups

conda create -n DyPs python=3.9
conda activate CoTa
pip install -r requirements.txt

Run Experiments

• Command to run our method

cd run
python run_lstm_hrl.py

• Command for baselines

cd run
python run_hrl.py   # Our method without lstm
python run_cvae.py  # Our method withour hierarchical structure
python PS_noid.py   # Naive parameter sharing 

Visualizations

• You can visualize the learning curves by tensorboard.

tensorboard --logdir logs

• You can visualize the city map and demand-supply heat map by following jupyters.

plot/grid_map.ipynb

• training curve of ride-hailing (2) scenario