A dynamic elevator control algorithm trained using reinforcement learning.
Created for the CS 229 Fall 2022 final project by Tejas Narayanan and Kiran Bhat. Read the paper here.
RLevator was developed using Python 3.10. To install all dependencies, run pip install -r requirements.txt.
To train an agent, run python train_elevator_agent.py. Argument details are as follows:
usage: train_elevator_agent.py [-h] [--num_floors_start NUM_FLOORS_START] [--num_floors_end NUM_FLOORS_END] [--timesteps TIMESTEPS] [--seed SEED] [--verbose VERBOSE]
options:
-h, --help show this help message and exit
--num_floors_start NUM_FLOORS_START, -nfs NUM_FLOORS_START
Number of floors at start
--num_floors_end NUM_FLOORS_END, -nfe NUM_FLOORS_END
Number of floors at end
--timesteps TIMESTEPS, -t TIMESTEPS
Number of timesteps to train
--seed SEED, -s SEED Random seed to use
--verbose VERBOSE, -v VERBOSE
Verbosity (0 or 1)
To monitor training progress, run tensorboard --logdir tensorboard.
When num_floors_end > num_floors_start, curriculum learning is applied to progressively increase the
complexity of the environment.
This program will train an agent and save it to a path corresponding to the environment parameters. This path is printed at the beginning of training, and can be passed into the benchmarking script to evaluate performance.
To compare the performance of trained agents to the standard elevator control algorithm,
run python benchmark_agents.py -n NUM_FLOORS --models MODEL_1_PATH MODEL_2_PATH ....
Argument details are as follows:
usage: benchmark_agents.py [-h] [--num_floors NUM_FLOORS] --models MODELS [MODELS ...]
options:
-h, --help show this help message and exit
--num_floors NUM_FLOORS, -n NUM_FLOORS
number of floors for environment
--models MODELS [MODELS ...], -m MODELS [MODELS ...]
Paths to models to be evaluated. Example path: models/env_v7/elev1-1_floor3-3_rand1/dd23f6.zip
--animation_delay ANIMATION_DELAY, -a ANIMATION_DELAY
Animation delay (in seconds) between timesteps. Larger delay -> slower animation. Animation is off by default.
This program will evaluate the models provided, along with the standard control algorithm, and provide the mean and standard deviation of episodic rewards for each model.
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