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CSCE 642 Project


Setup

You can set up the environment using conda + pip or virtual env + pip, Python version 3.10.12 is required.

To install the packages run

pip install -r requirements.txt

Running the Agents

To run the agents you simply run

python3 main.py <args>

A full list of arguments can be found at the bottom, to produce our results we ran the following commands.

DQN

For Training: For DQN we used these hyperparameters with alpha set to 0.0, 0.5 and 1.0.

python3 main.py -s 5000 -n 1024 -l 0.0001 -g 0.99 -d 90 -E 0.9 -m 1000 -N 100 -B 32 -a <alpha> -L 5 -S ddqn -M train

And for Testing:

python3 main.py -S ddqn -M test -e 100 -t 1000

A2C

For Training:

python3 main.py -s 20000 -n 2048 -l 0.0001 -g 0.99 -d 90 -S a2c -M train

And for Testing:

python3 main.py -S a2c -M test -e 100 -t 1000

Note that for -M test we expect there to already be trained models for all 3 alpha values (0.0, 0.5, 1.0). Our trained models are already saved.

Plotting Data

Once the data has been collected:

python3 main.py -S <any_solver> -M plot_both

Note that plotting will only occur if the corresponding pickle files are present and in the correct directory.

Command-line Arguments

You must specify the -S and -M flags to specify training and which solver or testing. All other arguments are optional and have default values configured as described below. To list out all the arguments you can use the -h flag.

Argument Description Default Value
-e Number of episodes for evaluation (only needed with -M test) 100
-s Number of environment steps to train for 5000
-t Max steps per episode in testing (only needed with -M test) 1000
-n Number of dense neurons per layer 1024
-l Learning rate 0.0001
-g Discount factor 0.99
-d Max duration per episode (seconds) 90
-E Initial Epsilon for Epsilon-Greedy in DQN (We use linear decay to 0 over the first 5000 steps) 0.9
-m Max Replay Memory size for DQN 1000
-N Interval for updating target newtork in DQN (number of steps) 100
-B Batch Size for sampling for DQN Replay Memory 32
-a Alpha for Prioritized Experience Replay 0.0
-L Number of Dense Layers for DQN Q network 5
-S Which solver to run for training, one of ddqn or a2c None
-M Mode, either train, test, or plot_both None

Citation

The source code for the environment requests that we include this citation.

@misc{highway-env,
  author = {Leurent, Edouard},
  title = {An Environment for Autonomous Driving Decision-Making},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/eleurent/highway-env}},
}