# Simulation parameters MAX_STEPS = 100 MAX_PARTICLES = 300 TRIALS_PARTICLES = 10 # Number of trials per particle size experiment OUTFILE = 'data/mcpomdp_metrics.csv' GUI_ON = False #Metrics - num_particles, iterations, time, goal_dist, error_mean, error_std printer(['particles','iters','rtime','goaldist','errormean','errorstd'], OUTFILE) for num_particles in np.arange(1,MAX_PARTICLES): for _ in range(TRIALS_PARTICLES): sim_params['num_particles'] = num_particles # Initialize model of world sim = Simulation(world_params) sstate = sim.get_state() # Reinitialize randomness after world generation np.random.seed(None) # Add a car model robot init_state = [5,5,0] sim.add_robot(robot_params,init_state) # add a radar sensor robot_id = robot_params['id'] sim.add_sensor(robot_id, sensor_params) readings = sim.read_sensors()
from model.Simulation import Simulation from view.render2d import View #import matplotlib.pyplot as plt #import numpy as np sim_params = {'world_type':'maze', 'world_length':100, 'world_width':100, 'robot_length':2, 'robot_stochastic':False, 'radar_radius':8,} robot_id = 1 v = View() sim = Simulation(sim_params) state = sim.get_state() v.update_rstate(state) sim.add_robot('car',robot_id) state = sim.get_state() v.update_rstate(state) sim.add_sensor(robot_id, 'radar') readings = sim.read_sensors() print readings raw_input('pause : press any key to finish...') #maze = gen_terrain_dfs(100,100) #world = Maze()