Major update

utils/__init__.py

@@ -1,5 +1,6 @@
 from config import *
 from normalizer import *
+from run import *
 try:
     from tf_logger import Logger
 except:

utils/config.py

@@ -46,3 +46,4 @@ class Config:
         self.master_optimizer_fn = None
         self.num_heads = 10
         self.min_epsilon = 0
+        self.save_interval = 0

utils/run.py

@@ -0,0 +1,53 @@
+#######################################################################
+# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com)    #
+# Permission given to modify the code as long as you keep this        #
+# declaration at the top                                              #
+#######################################################################
+
+import numpy as np
+import pickle
+
+
+def run_episodes(agent):
+    config = agent.config
+    window_size = 100
+    ep = 0
+    rewards = []
+    steps = []
+    avg_test_rewards = []
+    agent_type = agent.__class__.__name__
+    while True:
+        ep += 1
+        reward, step = agent.episode()
+        rewards.append(reward)
+        steps.append(step)
+        avg_reward = np.mean(rewards[-window_size:])
+        config.logger.info('episode %d, reward %f, avg reward %f, total steps %d, episode step %d' % (
+            ep, reward, avg_reward, agent.total_steps, step))
+
+        if config.save_interval and ep % config.save_interval == 0:
+            with open('data/%s-%s-online-stats-%s.bin' % (
+                    agent_type, config.tag, agent.task.name), 'wb') as f:
+                pickle.dump([steps, rewards], f)
+
+        if config.episode_limit and ep > config.episode_limit:
+            break
+
+        if config.test_interval and ep % config.test_interval == 0:
+            config.logger.info('Testing...')
+            agent.save('data/%s-%s-model-%s.bin' % (agent_type, config.tag, agent.task.name))
+            test_rewards = []
+            for _ in range(config.test_repetitions):
+                test_rewards.append(agent.episode(True))
+            avg_reward = np.mean(test_rewards)
+            avg_test_rewards.append(avg_reward)
+            config.logger.info('Avg reward %f(%f)' % (
+                avg_reward, np.std(test_rewards) / np.sqrt(config.test_repetitions)))
+            with open('data/%s-%s-all-stats-%s.bin' % (agent_type, config.tag, agent.task.name), 'wb') as f:
+                pickle.dump({'rewards': rewards,
+                             'steps': steps,
+                             'test_rewards': avg_test_rewards}, f)
+            if avg_reward > agent.task.success_threshold:
+                break
+
+    return steps, rewards, avg_test_rewards