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This is the source code of COMP 767 group project of Tianyu Shi & Jiawei Wang.

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This is the source code of COMP 767 group project of Tianyu Shi & Jiawei Wang.

Code structure:

Our main implementation of this model is in dgn_xxx (xxx represents for different scenario, e.g. ring network, figure eight network, or minicity network)

In each folder, you can find e.g. xxx_main-DGN.py this is the file to run;

In DGN.py, we define the main network structure and training process;

In xxx_Env.py we define the simulation environment.

Also, we implemented transportation method (Intelligent driver model), RL methods (DDPG, multi-agent version of PPO) in three different scenarios.

In explore folder, we saved some models that we tested, include different model structures, number of agents, etc.

How to reproduce:

Our model structure is : structure

Some parameters of models in the experiment settings:

Models config units of encoder layer activation function clip ratio discount factor Optimizer softupdate parameter learning rate(actor+critic) max returns
DGN a (128 , 128) ReLU 0.3 0.9 adam 0.01 (1e-4 , 1e-4) 2982.97
DGN b (512 , 128) ReLU 0.3 0.9 adam 0.01 (1e-4 , 1e-4) 2956.09
DGN c (128 , 64,128) ReLU 0.3 0.9 adam 0.01 (1e-4 , 1e-4)(1e-4 , 1e-4) 2900.04
DGN d (128 , 128) ReLU 0.15 0.9 adam 0.01 (1e-4 , 1e-4) 2972.85
DGN e (128 , 128) eLU 0.3 0.9 adam 0.01 (1e-4 , 1e-4) 2900.04
DGN f (128 , 128) ReLU 0.3 0.9 adam 0.01 (2.5e-4 , 1e-4) 2898.77
DDPG (128 , 128) eLU 0.3 0.9 adam 0.01 (2.5e-4 , 1e-4) 2660.89
MAPPO (128 , 128) ReLU 0.3 0.9 adam 0.01 1e-4 , 1e-4) 2975.76

You can run the python files with these above configurations with 100 runs, the triaining perfomance averaged over 10 different seeds is shown in the following figure: returns

Requirements:

We use the environment exactly the same as the conda environment in Flow, you can install Flow by following this Instruction:

Results:

Simulations

Models

Contact:

Tianyu Shi(tianyu.shi3@mail.mcgill.ca)

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This is the source code of COMP 767 group project of Tianyu Shi & Jiawei Wang.

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