wingedsheep’s gists

wingedsheep / Deep Q

Created August 20, 2016 10:50

Deep Q learning with experience replay and target network

	# import os
	# os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32"
	# import theano

	# import the neural net stuff
	from keras.models import Sequential
	from keras import optimizers
	from keras.layers.core import Dense, Dropout, Activation, Flatten
	from keras.layers.convolutional import Convolution1D
	from keras.layers.normalization import BatchNormalization

wingedsheep / LICENSE

Last active June 5, 2023 17:16

Simulated annealing to train NN

	MIT License

	Copyright (c) 2020 Vincent Bons

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is
	furnished to do so, subject to the following conditions:

wingedsheep / evolution.py

Created June 19, 2016 11:35

Simple Neuroevolution Cartpole

	# import os
	# os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32"
	# import theano

	# import the neural net stuff
	from keras.models import Sequential
	from keras import optimizers
	from keras.layers.core import Dense, Dropout, Activation, Flatten
	from keras.layers.convolutional import Convolution1D
	from keras.layers.normalization import BatchNormalization

wingedsheep / deepq.py

Created May 23, 2016 05:49

	# import os
	# os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32"
	# import theano

	# import the neural net stuff
	from keras.models import Sequential
	from keras import optimizers
	from keras.layers.core import Dense, Dropout, Activation
	from keras.layers.normalization import BatchNormalization
	from keras.layers.advanced_activations import LeakyReLU

wingedsheep / deepq.py

Created May 22, 2016 08:36

Deep Q learning for the lunar lander


	# import the neural net stuff
	from keras.models import Sequential
	from keras import optimizers
	from keras.layers.core import Dense, Dropout, Activation
	from keras.layers.normalization import BatchNormalization
	from keras.layers.advanced_activations import LeakyReLU
	from keras.regularizers import l2

	# import other stuff

wingedsheep / cartpole_runnner.py

Created May 21, 2016 14:29

Q learning cartpole with target network and experience replay

wingedsheep / cartpole_runner.py

Created May 20, 2016 17:45

Vincent Bons wingedsheep