longview-deep-learning

Some simple reinforcement learning examples for longview students.

Preparation instructions for MacOS: (best of luck for windows users... (it shouldn't be too hard))

In general you will need python3.6, tensorflow, gym. You can install those any way

you want or you can follow the instructions below for installing python via homebrew.

I don't have a blank machine to test this on but it will probably work. :)

you can simply type "bash README.md" as this README is also a bash script.

This may take a while as it will have to download and install Xcode if it is not already

installed on your machine.

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" brew install --upgrade python3 echo echo "this should show python 3.6.4 or later" python --version echo echo brew install cmake pip3 install virtualenv virt_dir=~/virtualenvs/tf_dev virtualenv -p /usr/bin/python3.6 $virt_dir pip install -r requirements.txt

echo "You will need to 'activate' your script by typing (you can use cut/paste to paste the line):" echo "source activate $virt_dir/bin/activate"

To run the pong game. This will start with an untrained network. It will take a while (several hours at least)

to train. You can uncomment the 'p.render()' line to watch it though that is slow. if you stop it and

restart the game it will pick up from where it left off (mostly). On a restart it has to repopulate the memory before

it can resume learning.

. $virt_dir/bin/activate

cd pong

python pong.py

This will show you what a trained model looks like when playing pong. It's pretty good but is clearly exploiting

a weakness in the opponent.

python saved_pong.py --checkpoint_dir saved_model

Another use of the saved_pong.py is to check in on the progress of

the learning. You can specify the checkpoint directory and it will

read the latest checkpoint of the running pong game and use that

to play. This does not interfere with the learning job.

python saved_pong.py --checkpoint_dir ../checkpoints/pong-simple

to run the apples game in interactive mode:

. $virt_dir/bin/activate

cd apples

python interactive_apples.py

take a look at interactive_apples.py: on_key_press() to see keyboard commands

this runs the apples game without doing any rendering. One 'to do'

item for me is to refactor the apples_game into something which has

the same interface as the openai stuff. It's a little tricky, I'll

have to deal with the rendering stuff in some special way as just

importing the graphics library causes it to fail if it cannot find

display. That's why I have batch_apples.py, it never imports the

graphics library (arcade).

python batch_apples.py

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.idea		.idea
apples		apples
pong		pong
README.md		README.md
policy_gradient.py		policy_gradient.py
requirements.txt		requirements.txt
tf_helpers.py		tf_helpers.py
timings.py		timings.py

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longview-deep-learning

Some simple reinforcement learning examples for longview students.

Preparation instructions for MacOS: (best of luck for windows users... (it shouldn't be too hard))

In general you will need python3.6, tensorflow, gym. You can install those any way

you want or you can follow the instructions below for installing python via homebrew.

I don't have a blank machine to test this on but it will probably work. :)

you can simply type "bash README.md" as this README is also a bash script.

This may take a while as it will have to download and install Xcode if it is not already

installed on your machine.

To run the pong game. This will start with an untrained network. It will take a while (several hours at least)

to train. You can uncomment the 'p.render()' line to watch it though that is slow. if you stop it and

restart the game it will pick up from where it left off (mostly). On a restart it has to repopulate the memory before

it can resume learning.

. $virt_dir/bin/activate

cd pong

python pong.py

This will show you what a trained model looks like when playing pong. It's pretty good but is clearly exploiting

a weakness in the opponent.

python saved_pong.py --checkpoint_dir saved_model

Another use of the saved_pong.py is to check in on the progress of

the learning. You can specify the checkpoint directory and it will

read the latest checkpoint of the running pong game and use that

to play. This does not interfere with the learning job.

python saved_pong.py --checkpoint_dir ../checkpoints/pong-simple

to run the apples game in interactive mode:

. $virt_dir/bin/activate

cd apples

python interactive_apples.py

take a look at interactive_apples.py: on_key_press() to see keyboard commands

this runs the apples game without doing any rendering. One 'to do'

item for me is to refactor the apples_game into something which has

the same interface as the openai stuff. It's a little tricky, I'll

have to deal with the rendering stuff in some special way as just

importing the graphics library causes it to fail if it cannot find

display. That's why I have batch_apples.py, it never imports the

graphics library (arcade).

python batch_apples.py

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