def plt_heat_map_results(plt_file_name = None): opt = Options() opt.disp_on = False pob_siz_len = np.arange(start=3, stop=11,step=2) #not even steps needed hist_len = np.arange(start=1, stop=5) # pob_siz_len =[5,3,5,9,1] results_success_rate_map_zero = [] results_astar_diff = [] cnt_Test=0 with K.get_session(): for p in pob_siz_len: opt.pob_siz = p opt.state_siz = (p * opt.cub_siz) ** 2 print("start with opt.pob_siz: {}".format(opt.pob_siz)) print("start with opt.state_siz: {}".format(opt.state_siz)) # get_data(opt)#generaten new data for l in hist_len: opt.hist_len = l print("start with opt.hist_len: {}".format(opt.hist_len)) # train_model(opt, mdl_name,epochs=EPOCHS) # [success_rate, astar_diff] = test_model(opt,mdl_name) # results_success_rate_map_zero.append(success_rate) results_success_rate_map_zero.append(cnt_Test) cnt_Test1+=1 # results_astar_diff.append(astar_diff) results_success_rate_map_zero=np.array(results_success_rate_map_zero) plt.imshow(results_success_rate_map_zero.reshape(len(pob_siz_len),len(hist_len)), cmap='hot', interpolation='nearest') plt.colorbar() # plt.show() helper_save(plt_file_name)
default=1000) parser.add_argument("-ss", "--silent", help="Runs test without displaying the simulation", action="store_false") args = parser.parse_args() model_path = args.model # -------------------------------------------------------- # 0. initialization opt = Options() sim = Simulator(opt.map_ind, opt.cub_siz, opt.pob_siz, opt.act_num) opt.disp_on = args.silent if args.steps: opt.steps = args.steps # -------------------------------------------------------- # Input layer image_dimension = opt.cub_siz * opt.pob_siz img_rows = img_cols = image_dimension input_shape = [img_rows, img_cols, opt.hist_len] # -------------------------------------------------------- # Model agent = model.model(input_shape) agent.load_weights(model_path) if not agent:
import numpy as np np.random.seed(0) from random import randrange # custom modules from utils import Options, rgb2gray from simulator import Simulator # 0. initialization opt = Options() sim = Simulator(opt.map_ind, opt.cub_siz, opt.pob_siz, opt.act_num) states = np.zeros([opt.data_steps, opt.state_siz], float) labels = np.zeros([opt.data_steps], int) # Note I am forcing the display to be off here to make data collection fast # you can turn it on again for debugging purposes opt.disp_on = False # 1. control loop if opt.disp_on: win_all = None win_pob = None epi_step = 0 # #steps in current episode nepisodes = 1 # total #episodes executed state = sim.newGame(opt.tgt_y, opt.tgt_x) for step in range(opt.data_steps): if state.terminal or epi_step >= opt.early_stop: epi_step = 0 nepisodes += 1 state = sim.newGame(opt.tgt_y, opt.tgt_x) else:
args = parser.parse_args() args.func(args) '''if not args.cfg_file: opt.prefix = args.prefix opt.set_algo(args.algo) opt.steps = args.steps opt.mtype = args.mtype opt.rtype = args.rtype opt.hist_len = args.hlength from_file = False else: opt.load_config_file(args.file) from_file = True''' opt.disp_on = args.visualize plot = args.plot_output model_name = opt.generate_model_name() run_name = opt.generate_run_name() # Environment setup opt.disp_on = args.visualize sim = Simulator(opt.map_ind, opt.cub_siz, opt.pob_siz, opt.act_num) if opt.rtype == opt.supported_rtypes[0]: opt.training = True else: opt.testing = True # Default model architecture layer_params = [
hours = int(sec / 3600) rem = int(sec - (hours * 3600)) mins = rem / 60 rem = rem - (mins * 60) secs = rem print 'Training time:', hours, ':', mins, ':', secs #Save the weights model.save_weights(opt.weights_fil, overwrite=True) print('Saved weights') with open(opt.network_fil, "w") as outfile: json.dump(model.to_json(), outfile) #Testing opt.disp_on = True win_all = None win_pob = None action = np.argmax( model.predict((state_with_history).reshape(1, img_rows, img_cols, opt.hist_len))) state_with_history = np.zeros((opt.hist_len, opt.state_siz)) append_to_hist(state_with_history, rgb2gray(state.pob).reshape(opt.state_siz)) next_state_with_history = np.copy(state_with_history) epi_step = 0 nepisodes = 0 n_reached = 0.0 reward_acc_test = 0 reward_acc_list_test = [] print('Test Phase')
parser.add_argument( "-s", "--steps", help="(Optional) Number of steps to train the model for. Default is 1000", type=int, default=10000) parser.add_argument("-ss", "--silent", help="Runs test without displaying the simulation", action="store_false") args = parser.parse_args() # -------------------------------------------------------- # 0. initialization opt = Options() opt.disp_on = True sim = Simulator(opt.map_ind, opt.cub_siz, opt.pob_siz, opt.act_num) # Display if opt.disp_on: win_all = None win_pob = None opt.disp_on = args.silent opt.steps = args.steps run_name = args.model sess = tf.Session() agent = DqnAgent(sess, is_duelling_dqn=False) sess.run(tf.global_variables_initializer())