def build_summaries():
episode_reward = tf.Variable(0.)
scalar_summary("Reward", episode_reward)
episode_ave_max_q = tf.Variable(0.)
scalar_summary("Qmax Value", episode_ave_max_q)
logged_epsilon = tf.Variable(0.)
scalar_summary("Epsilon", logged_epsilon)
# Threads shouldn't modify the main graph, so we use placeholders
# to assign the value of every summary (instead of using assign method
# in every thread, that would keep creating new ops in the graph)
summary_vars = [episode_reward, episode_ave_max_q, logged_epsilon]
summary_placeholders = [tf.placeholder("float")
for i in range(len(summary_vars))]
assign_ops = [summary_vars[i].assign(summary_placeholders[i])
for i in range(len(summary_vars))]
summary_op = merge_all_summaries()
return summary_placeholders, assign_ops, summary_op
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