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Modify evaluation score processing #4

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Modify evaluation score processing #4

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22 changes: 14 additions & 8 deletions DQN.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1112,21 +1112,27 @@
" eval_rewards.append(episode_reward_sum)\n",
" gif = False # Save only the first game of the evaluation as a gif\n",
" \n",
" print(\"Evaluation score:\\n\", np.mean(eval_rewards)) \n",
" try:\n",
" generate_gif(frame_number, frames_for_gif, eval_rewards[0], PATH)\n",
" except IndexError:\n",
" print(\"No evaluation game finished\")\n",
" if eval_rewards:\n",
" eval_score = np.mean(eval_rewards)\n",
" first_episode_reward_sum = eval_rewards[0]\n",
" # When the first game of the evaluation is not finished\n",
" # (this would happen if the agent got stuck in a loop)\n",
" else:\n",
" eval_score = episode_reward_sum\n",
" first_episode_reward_sum = episode_reward_sum\n",
"\n",
" print(\"Evaluation score:\\n\", eval_score)\n",
" generate_gif(frame_number, frames_for_gif, first_episode_reward_sum, PATH)\n",
" \n",
" #Save the network parameters\n",
" # Save the network parameters\n",
" saver.save(sess, PATH+'/my_model', global_step=frame_number)\n",
" frames_for_gif = []\n",
" \n",
" # Show the evaluation score in tensorboard\n",
" summ = sess.run(EVAL_SCORE_SUMMARY, feed_dict={EVAL_SCORE_PH:np.mean(eval_rewards)})\n",
" summ = sess.run(EVAL_SCORE_SUMMARY, feed_dict={EVAL_SCORE_PH:eval_score})\n",
" SUMM_WRITER.add_summary(summ, frame_number)\n",
" with open('rewardsEval.dat', 'a') as eval_reward_file:\n",
" print(frame_number, np.mean(eval_rewards), file=eval_reward_file)"
" print(frame_number, eval_score, file=eval_reward_file)"
]
},
{
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