WildfireDRL_Scripts

Scripts for training and evaluating neural networks used in the Wildfire DRL papers

Overview

The code is divided into separate files:

• TrainNetwork.py: Trains the neural network controller

• ReplayMemory.py: Represents the Q-learning replay memory

• QNetwork.py: Contains all of the neural network code (Keras running on Theano), including an object for training Q-Networks and an object to read and evaluated trained network HDF5 files from Keras

• Simulation.py: Represents the simulation environment

• AircraftModel.py: Represents the aircraft state, dynamics, and observation model

• Filters.py: Describes the EKF and particle filter approachs for filtering noisy camera observations

• WildfireModels.py: Contains methods for a stochastic wildfire model

Prerequisites

These files use python with the following dependencies:

• numpy
• math
• Theano
• Keras (using the Theano backend)