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Deep Reinforcement Learning Hands-On
book

Deep Reinforcement Learning Hands-On

by Oleg Vasilev, Maxim Lapan, Martijn van Otterlo, Mikhail Yurushkin, Basem O. F. Alijla
June 2018
Intermediate to advanced content levelIntermediate to advanced
546 pages
13h 30m
English
Packt Publishing
Content preview from Deep Reinforcement Learning Hands-On

Experiment results

In this section, we'll take a look at the results of our multi-step training process

The baseline agent

To train the agent, run Chapter17/01_a2c.py with the optional --cuda flag to enable GPU and required -n option with the experiment name used in TensorBoard and in a directory name to save models.

Chapter17$ ./01_a2c.py --cuda -n tt AtariA2C ( (conv): Sequential ( (0): Conv2d(2, 32, kernel_size=(8, 8), stride=(4, 4)) (1): ReLU () (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2)) (3): ReLU () (4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1)) (5): ReLU () ) (fc): Sequential ( (0): Linear (3136 -> 512) (1): ReLU () ) (policy): Linear (512 -> 4) (value): Linear (512 -> 1) ) 4: done 13 episodes, mean_reward=0.00, best_reward=0.00, ...
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Publisher Resources

ISBN: 9781788834247Supplemental Content