Only show log if there is a "offline" history.

environment_wrapper.py

@@ -77,7 +77,6 @@ def one_episode(environment, agent, render, learn, conf=None, max_steps=1000):
 def learn_offline(agent, conf):
     """ Train the agent with its memories. """
     print('Learning with ', len(agent.memory.history), ' memories.')
-    agent.epsilon = agent.epsilon_min
 
     score_history = []
     avg_score_history = []
@@ -89,6 +88,7 @@ def learn_offline(agent, conf):
         pbar.set_description(desc)
         pbar.refresh()
         if i % conf.offline_validate_every_x_iteration == 1 and conf.offline_validate_every_x_iteration is not -1:
+            agent.epsilon = agent.epsilon_min
             score = one_episode(conf.env, agent, conf.render, False, conf=conf)
             score_history.append(score)
             is_solved = np.mean(score_history[-25:])
@@ -96,6 +96,7 @@ def learn_offline(agent, conf):
             avg_score_history.append(is_solved)
             if is_solved > IS_SOLVED:
                 break
+    if conf.offline_validate_every_x_iteration is not -1:
         process_logs(avg_score_history, score_history, conf)