TensorFlow Reinforcement Learning Quick Start Guide by Kaushik Balakrishnan

Author:Kaushik Balakrishnan
Language: eng
Format: mobi, epub
Tags: COM062000 - COMPUTERS / Data Modeling and Design, COM037000 - COMPUTERS / Machine Theory, COM004000 - COMPUTERS / Intelligence (AI) and Semantics
Publisher: Packt
Published: 2019-03-29T15:54:53+00:00

Running a Rainbow network on Dopamine

In 2018, some engineers at Google released an open source, lightweight, TensorFlow-based framework for training RL agents, called Dopamine. Dopamine, as you may already know, is the name of an organic chemical that plays an important role in the brain. We will use Dopamine to run Rainbow.

The Dopamine framework is based on four design principles:

Easy experimentation

Flexible development

Compact and reliable

Reproducible

To download Dopamine from GitHub, type the following command in a Terminal:

git clone https://github.com/google/dopamine.git

We can test whether Dopamine was successfully installed by typing the following commands into a Terminal:

cd dopamine

export PYTHONPATH=${PYTHONPATH}:.

python tests/atari_init_test.py

The output of this will look something like the following:

2018-10-27 23:08:17.810679: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

2018-10-27 23:08:18.079916: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero

2018-10-27 23:08:18.080741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties:

name: GeForce GTX 1060 with Max-Q Design major: 6 minor: 1 memoryClockRate(GHz): 1.48

pciBusID: 0000:01:00.0

totalMemory: 5.93GiB freeMemory: 5.54GiB

2018-10-27 23:08:18.080783: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0

2018-10-27 23:08:24.476173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:

2018-10-27 23:08:24.476247: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958] 0

2018-10-27 23:08:24.476273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: N

2018-10-27 23:08:24.476881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5316 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 6.1)

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Ran 2 tests in 8.475s

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