minor edits

manual_cl.py

@@ -22,7 +22,6 @@
 
 
 def alter_env(exp_type, variant):
-	print('LOGGER: alter_env_start')
 	xml_path = os.path.join(gym_real.__path__[0], "envs/assets/real.xml")
 
 	tree = ET.parse(xml_path)
@@ -38,21 +37,17 @@ def alter_env(exp_type, variant):
 			pos.set("pos", str(variant) + " 0 "  + str(abs(-0.1) + 0.7))
 
 	tree.write(xml_path)
-	print('LOGGER: alter_env_end')
 
 def load_checkpoint(checkpoint, run_path):
-	print('LOGGER: load_checkpoint_start')
 	checkpoint_log = run_path + '/checkpoint'
 	checkpoint_keeper = []
 	with open(checkpoint_log) as csv_file:
 		csv_reader = csv.reader(csv_file)
 		for row in csv_reader:
 			checkpoint_keeper.append(row[0])
-	print('LOGGER: load_checkpoint_end')
 	return run_path + '/' + checkpoint_keeper[checkpoint] + '.pkl'
 
 def log_experiments(exp_num, exp_type, variants, model_names, exp_log, log_dict):
-	print('LOGGER: log_experiments_start')
 	file = shelve.open(exp_log)
 	file['exp' + exp_num] = [exp_type, variants, model_names]
 	file.close()
@@ -62,38 +57,31 @@ def log_experiments(exp_num, exp_type, variants, model_names, exp_log, log_dict)
 	# 	if os.stat(exp_log).st_size == 0:
 	# 		csv_writer.writerow(['exp', 'type', 'variants', 'models'])
 	# 	csv_writer.writerow(['exp' + str(exp_num), exp_type, variants, model_names])
-	print('LOGGER: log_experiments_end')
 
 def run_experiment(exp_num, exp_type, variants, n_cpu, step_total, exp_log, log_dict):
-	print('LOGGER: run_experiments_start')
 	model_names = []
 	run_path = ''
 	for order, variant in enumerate(variants):
-		print('LOGGER: run_experiments_mid')
 		alter_env(exp_type, variant)
 		env = gym.make("Real-v0")
 		env = Monitor(env, 'tf_save', allow_early_resets=True)
 		env = SubprocVecEnv([lambda: env for i in range(n_cpu)])
-		print('LOGGER: run_experiments_mid2')
 		if order == 0:
-			model = PPO2(MlpPolicy, env, verbose=1, tensorboard_log="./tensorboard_log/")
+			model = PPO2(MlpPolicy, env, verbose=0, tensorboard_log="./tensorboard_log/")
 		else:
 			load_name = load_checkpoint(-1, run_path)
 			model = PPO2.load(load_name, env=env)
-		print('LOGGER: run_experiments_mid3')
 		model_names.append(model.model_name)
 		run_path = model.graph_dir
 		model.learn(total_timesteps=step_total)
 		env.close()
 		del model, env
-		print('LOGGER: run_experiments_mid4')
 	log_experiments(exp_num, exp_type, variants, model_names, exp_log, log_dict)
-	print('LOGGER: run_experiments_end')
 
 if __name__ == "__main__":
-	n_cpu = 8
+	n_cpu = 20
 	n_step = 128
-	desired_log_pts = 1500
+	desired_log_pts = 1000
 	step_total = desired_log_pts * n_cpu * n_step
 	leg_lengths = [i * -0.1 for i in range(1, 5)]
 	goal_diss = [i * -2 for i in range(2, 6)]
@@ -105,7 +93,6 @@ def run_experiment(exp_num, exp_type, variants, n_cpu, step_total, exp_log, log_
 	dis_type = 'GOAL_DIS'
 	exp_types = [leg_type, dis_type]
 	for i in range(5):
-		print('LOGGER: main_loop_start')
 		for j, exp_type in enumerate(exp_types):
 			if exp_type == leg_type:
 				variant = leg_lengths
@@ -114,8 +101,6 @@ def run_experiment(exp_num, exp_type, variants, n_cpu, step_total, exp_log, log_
 			perm_2 = list(permutations(variant, 2))
 			perm_4 = list(permutations(variant))
 			perms = perm_2 + perm_4
-			print('LOGGER: main_loop_mid')
 			for k, perm in enumerate(perms):
-				print('LOGGER: main_loop_midstart')
 				run_experiment(str(i) + '_' + str(j) + '_' + str(k), exp_type, perm, n_cpu, step_total, exp_log, log_dict)
 

plotter.py

@@ -55,6 +55,7 @@ def plot_graph(plot_name, save_name, length, reward):
     plt.savefig(save_name + ".png")
     plt.savefig(save_name + ".eps")
     # plt.show()
+    plt.close()
 
 plot_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "plot_saves/")
 os.makedirs(plot_dir, exist_ok=True)