add main.py

Author: YangRui2015

main.py

@@ -0,0 +1,122 @@
+# -*- coding: utf-8 -*-
+# main.py
+# author: yangrui
+# description: 
+# created: 2019-05-10T19:01:15.155Z+08:00
+# last-modified: 2020-03-13T10:00:30.182Z+08:00
+# email: yangrui19@mails.tsinghua.edu.cn
+
+from global_utils import print_summary
+from options import parse_options
+from global_utils import set_global_seed, save_performance, plot_data
+import time
+from agent_env_params import design_agent_and_env
+from multiprocessing import Process
+import random
+
+from environment import Environment
+from agent import Agent
+
+
+def run_HAC(FLAGS,env,agent, plot_figure=False, num=0):
+ from global_utils import save_plot_figure # import here is for mutilprocessing
+
+ NUM_EPOCH = FLAGS.num_epochs 
+ SAVE_FREQ = FLAGS.save_freq 
+ # Print task summary
+ print_summary(FLAGS, env)
+
+ if not FLAGS.test:
+ num_episodes = FLAGS.num_exploration_episodes 
+ else:
+ num_episodes = FLAGS.num_test_episodes
+ NUM_EPOCH = 1 # only test 1 epoch
+
+ performance_list = []
+ test_performance_list = []
+ if FLAGS.curriculum >= 2:
+ curriculum_epoch = NUM_EPOCH / FLAGS.curriculum
+ assert curriculum_epoch == int(curriculum_epoch), 'NUM_EPOCH / FLAGS.curriculum should be int' 
+ 
+ for epoch in range(1, NUM_EPOCH + 1): 
+ successful_episodes = 0
+ if not FLAGS.test and FLAGS.curriculum >= 2:
+ env.set_goal_range(env_params['curriculum_list'][int((epoch - 1) // curriculum_epoch)])
+
+ for episode in range(num_episodes): 
+ print("\nEpoch %d, Episode %d" % (epoch, episode))
+ # Train for an epoch
+ success = agent.train(env, epoch * num_episodes + episode,test=FLAGS.test)
+ if success:
+ print("End Goal Achieved\n")
+ successful_episodes += 1
+ # Save agent
+ if epoch % SAVE_FREQ == 0 and not FLAGS.test and FLAGS.threadings == 1:
+ agent.save_model(epoch * num_episodes)
+ success_rate = successful_episodes / num_episodes * 100
+ print("\nEpoch %d, Success Rate %.2f%%" % (epoch, success_rate))
+ performance_list.append(success_rate)
+
+ if not FLAGS.test: 
+ success_test = 0
+ if FLAGS.curriculum >= 2:
+ env.set_goal_range(env_params['curriculum_list'][-1])
+
+ print('\ntesting for %d episodes' % (FLAGS.num_test_episodes))
+ for episode in range(FLAGS.num_test_episodes):
+ success = agent.train(env, episode, test=True)
+ success_test += int(success)
+ success_rate = success_test / FLAGS.num_test_episodes * 100
+ print('testing accuracy: %.2f%%' % (success_rate))
+ test_performance_list.append(success_test)
+
+ if plot_figure:
+ save_plot_figure(performance_list)
+ save_plot_figure(test_performance_list, name='test-performance.jpg')
+
+ save_performance(performance_list, test_performance_list, FLAGS=FLAGS, thread_num=num)
+ if FLAGS.save_experience:
+ agent.save_experience()
+ 
+
+
+def worker(agent_params, env_params, FLAGS, i):
+ seed = int(time.time()) + random.randint(0, 100)
+ set_global_seed(seed)
+ FLAGS.seed = seed
+ env = Environment(env_params, FLAGS)
+ agent = Agent(FLAGS, env, agent_params)
+ run_HAC(FLAGS, env, agent, plot_figure=False, num=i)
+
+
+FLAGS = parse_options()
+agent_params, env_params = design_agent_and_env(FLAGS)
+
+assert FLAGS.threadings >= 1, "Threadings should be more than 1!"
+if FLAGS.threadings == 1:
+ seed = int(time.time()) + random.randint(0,100)
+ set_global_seed(seed)
+ FLAGS.seed = seed
+ env = Environment(env_params, FLAGS)
+ agent = Agent(FLAGS, env, agent_params)
+ run_HAC(FLAGS, env, agent, plot_figure=True)
+else:
+ # parallel run
+ thread_list = []
+ for i in range(FLAGS.threadings):
+ p = Process(target=worker, args=(agent_params, env_params, FLAGS, i))
+ p.start()
+ thread_list.append(p)
+
+ for p in thread_list:
+ p.join()
+
+
+
+
+
+
+
+
+
+