July 2018
Intermediate to advanced
254 pages
8h 12m
English
Content preview from Hands-On Intelligent Agents with OpenAI Gym
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Testing performance of your deep Q-learner in Atari games
It feels great, doesn't it? You have now developed an agent that can learn to play any Atari game and get better at it by itself! Once you have your agent trained on any Atari game, you can use the test mode of the script to test the agent's performance based on its learning so far. You can enable the test mode by using the --test argument in the deep_q_learner.py script. It is useful to enable rendering of the environment, too, so that you can visually see (apart from the rewards printed on the console) how the agent is performing. As an example, you can test the agent in the Seaquest Atari game using the following command:
(rl_gym_book) praveen@ubuntu:~/HOIAWOG/ch6$ python deep_Q_learner.py ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.