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https://github.com/wassname/DeepRL.git
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Update async methods
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@@ -90,12 +90,20 @@ I use 8 threads and a two tanh hidden layer network, each hidden layer has 64 hi
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# Usage
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Detailed usage and all training parameters can be found in ```main.py```.
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And you need to create following directories before running the program:
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You need to create following directories before running the program:
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```
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cd DeepRL
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mkdir data log
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```
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Code is only tested in macOS 10.12 and CentO/S 6.8. And for CentO/S 6.8, you need
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```
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export OMP_NUM_THREADS=1
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```
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manually in shell before running parallelized implementation.
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# References
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* [Human Level Control through Deep Reinforcement Learning](https://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
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* [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)
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@@ -100,7 +100,7 @@ def async_pixel_atari(name):
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config.discount = 0.99
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config.target_network_update_freq = 10000
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config.max_episode_length = 10000
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config.num_workers = 10
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config.num_workers = 6
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config.update_interval = 20
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config.test_interval = 50000
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config.test_repetitions = 1
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@@ -120,7 +120,7 @@ def a3c_pixel_atari(name):
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config.worker = AdvantageActorCritic
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config.discount = 0.99
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config.max_episode_length = 10000
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config.num_workers = 10
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config.num_workers = 6
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config.update_interval = 20
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config.test_interval = 50000
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config.test_repetitions = 1
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@@ -270,7 +270,7 @@ if __name__ == '__main__':
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# gym.logger.setLevel(logging.DEBUG)
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gym.logger.setLevel(logging.INFO)
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dqn_cart_pole()
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# dqn_cart_pole()
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# async_cart_pole()
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# a3c_cart_pole()
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# a3c_continuous()
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@@ -282,7 +282,7 @@ if __name__ == '__main__':
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# dqn_pixel_atari('PongNoFrameskip-v4')
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# async_pixel_atari('PongNoFrameskip-v4')
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# a3c_pixel_atari('PongNoFrameskip-v4')
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a3c_pixel_atari('PongNoFrameskip-v4')
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# dqn_pixel_atari('BreakoutNoFrameskip-v4')
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# async_pixel_atari('BreakoutNoFrameskip-v4')
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