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docs update + removing unused code from parallel_actor
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## Motivation
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## Motivation
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Enable easy design and training of state-of-the-art reinforcement learning algorithms with multiple environments
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Train and evaluate reinforcement learning agents by harnessing the power of multi-core CPU processing to achieve state-of-the-art results. Provide a sandbox for easing the development process of new algorithms through a modular design and an elegant set of APIs.
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## Solution
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## Solution
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site_name: Reinforcement Learning Coach Documentation
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site_name: Reinforcement Learning Coach Documentation
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theme: material
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theme: readthedocs
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site_description: 'Reinforcement Learning Coach Documentation by Intel Nervana.'
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site_description: 'Reinforcement Learning Coach Documentation by Intel Nervana.'
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markdown_extensions:
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markdown_extensions:
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- mdx_math:
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- mdx_math:
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@@ -29,14 +29,6 @@ import shutil
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start_time = time.time()
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start_time = time.time()
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# Disables write_meta_graph argument, which freezes entire process and is mostly useless.
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class FastSaver(tf.train.Saver):
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def save(self, sess, save_path, global_step=None, latest_filename=None,
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meta_graph_suffix="meta", write_meta_graph=True):
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super(FastSaver, self).save(sess, save_path, global_step, latest_filename,
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meta_graph_suffix, False)
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if __name__ == "__main__":
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser = argparse.ArgumentParser()
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parser.add_argument('--ps_hosts',
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parser.add_argument('--ps_hosts',
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