Optimizing Dynamic Cellular network using Reinforcement Learning. This work is based on the framework of loadbalanceRL
- Python 3.6
- Ari Saha [email protected]
- Mingyang Liu [email protected]
To install loadbalanceRL:
$ virtualenv venv
$ make install-prod
$ source venv/bin/activate
$ cp -r loadbalanceRL/etc rainman/venv/lib/python3.6/site-packages/loadbalanceRL-1.0-py3.6.egg/loadbalanceRL
Here are the commands avaiable for loadbalanceRL.
(venv)$ loadbalanceRL --help
Using TensorFlow backend.
Rainman2's logging has been configured!
Usage: loadbalanceRL [OPTIONS] COMMAND [ARGS]...
Rainman2's cli
Options:
--verbose BOOLEAN show verbose output for debugging
--replace_target_iter FLOAT iteration steps to replace the params in the
target net in DQN
--batch_size FLOAT batch size for updating the evaluation net in
DQN
--memory_size FLOAT memory size for experience reply in DQN
--learning_rate FLOAT learning rate for the gradient method
--epsilon_min FLOAT min value for epsilon to stop updating
--epsilon_decay FLOAT rate at which epsilon gets updated
--epsilon FLOAT epsilon for epsilon-greedy policy
--gamma FLOAT discount factor
--alpha FLOAT learning rate
--episodes INTEGER numeber of episodes/epochs
Commands:
Cellular Arguments for cellular environment
Cellular Environment Experiments
================================
Cellular environments describe interactions of mobile devices (known as UEs) with cell towers (known as Access Points).
Rainman3 contains a simulated cellular environment called 'Dev' environment, which can be used to test various Reinforcement algorithms. Currently 'Dev' cellular environment supports the following algorithms: * Naive Qlearning, which is the generic form of Qlearning (Tabular). * Qlearning using linear function approximator, e.g. Linear regression. * Qlearning using non-linear function approximator, e.g. Neural Network. * Qlearning using Deep Q network, e.g. DQN.
To run experiments using Cellular network:
- If testing on development cellular network (i.e. --env_type = Dev), first start the development server (in a new terminal tab), which will instantiate a simulated cellular network.
(venv)$ cd loadbalanceRL/lib/environment/cellular/dev
(venv)$ python server.py
-
To start running experiments on Cellular network, use the command line as below.
(venv)$ loadbalanceRL Cellular --help Using TensorFlow backend. Rainman2's logging has been configured! Usage: loadbalanceRL Cellular [OPTIONS] COMMAND [ARGS]... Arguments for cellular environment Options: --env_type [Dev|Prod] type of cellular network: Dev/Prod --help Show this message and exit. Commands: qlearning_linear_regression Qlearning with Linear Regressor as Function... qlearning_naive Qlearning without any function approximator... qlearning_nn Qlearning with Neural Network as Function... qlearning_dqn DQN
-
With Tabular Q-learning algorithm
(venv)$ loadbalanceRL --verbose True --episodes 50 Cellular --env_type Dev qlearning_naive -
With Linear regression Q-learning algorithm
(venv)$ loadbalanceRL --verbose True --episodes 50 Cellular --env_type Dev qlearning_linear_regression -
With Neural network Q-learning algorithm
(venv)$ loadbalanceRL --verbose True --episodes 50 Cellular --env_type Dev qlearning_nn -
With DQN
(venv)$ loadbalanceRL --verbose True --episodes 50 Cellular --env_type Dev qlearning_dqn
(venv)$ jupyter notebook
- Ploat animation for dynamic environment with Q learning naive method
test_cellular_env.ipynb
- Ploat animation for dynamic environment with Q learning LinearRegression method
test_cellular_linear_dynamic.ipynb
- Ploat animation for dynamic environment with Q learning NN method
test_cellular_NN_dynamic.ipynb
- Ploat animation for dynamic environment with Q learning DQN method
test_cellular_dqn_dynamic.ipynb