env_file = open("Environment.txt", "w")
gridWorld = CreateEnvironment()
gridWorld.create(env_file,
size_row='10',
size_col='10',
agent_row=str(vehState[0]),
agent_col=str(vehState[1]),
goal_row=str(goal[0]),
goal_col=str(goal[1]),
static_number='2',
static_list=[0, 3, 2, 4])
env_file = open("Environment.txt", "r")
text_in_file = env_file.readline()
print(text_in_file)
grid = GridWorld(text_in_file)
gw = grid.gridDefine()
#-------------------------------------------------------
# initialize agent class and uav class
Agent = agent(vehState)
# define a model dictionary, which maps user inputs of learning model names to learning model function
modelType = {
"random": Agent.predict_Random,
"standard": Agent.predict_Standard,
"NN": Agent.predict_NN
}
UAV = uav(vehState)
# initialize decision model (options = "random", "standard", or "NN")
model = "random" # will be a user input
