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Last active October 3, 2017 05:22
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plot tensorforce explorations
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T04:28:26.308138Z",
"start_time": "2017-10-03T04:28:25.978072Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab --no-import-all inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T04:28:27.057563Z",
"start_time": "2017-10-03T04:28:26.309735Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"from tensorforce.core import explorations"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T04:30:50.255112Z",
"start_time": "2017-10-03T04:30:50.044588Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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uaU2HhtVJ+ySXXeX4IuvJgrL/S0HlLiJh7ear63Lz1XW9jhFwmnMXEQlBKncR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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7616634240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Before https://github.com/reinforceio/tensorforce/pull/149\n",
"epsilon_timesteps=10000\n",
"expl = explorations.EpsilonAnneal(epsilon=0.9, epsilon_final=0.1, epsilon_timesteps=epsilon_timesteps, start_after=1000)\n",
"x=np.linspace(0, epsilon_timesteps,10000)\n",
"y=[expl(episode=i, timestep=i) for i in x]\n",
"plt.plot(x,y)\n",
"plt.ylim([0,1])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T04:30:50.583542Z",
"start_time": "2017-10-03T04:30:50.386116Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XeszywswizWyZmc0NrDcLnLYiN3Aai5jA9gue1sLMngxs32Bmt3qzJ8Exs5pm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7615bbf3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# after https://github.com/reinforceio/tensorforce/pull/149\n",
"class EpsilonAnneal(explorations.Exploration):\n",
" \"\"\"\n",
" Annealing epsilon parameter based on ratio of current timestep to total timesteps.\n",
" \"\"\"\n",
"\n",
" def __init__(self, epsilon=1.0, epsilon_final=0.1, epsilon_timesteps=10000, start_after=0):\n",
" self.epsilon = epsilon\n",
" self.epsilon_final = epsilon_final\n",
" self.epsilon_timesteps = epsilon_timesteps\n",
" self.start_after = start_after\n",
"\n",
" def __call__(self, episode=0, timestep=0):\n",
" if timestep < self.start_after:\n",
" return self.epsilon\n",
"\n",
" offset = self.start_after\n",
"\n",
" self.epsilon = min(self.epsilon, max(\n",
" self.epsilon_final,\n",
" 1.0 - (timestep - offset) / (self.epsilon_timesteps - offset)\n",
" ))\n",
"\n",
" return self.epsilon\n",
" \n",
"epsilon_timesteps=10000\n",
"expl = EpsilonAnneal(epsilon=0.9, epsilon_final=0.1, epsilon_timesteps=epsilon_timesteps, start_after=1000)\n",
"x=np.linspace(0, epsilon_timesteps,10000)\n",
"y=[expl(episode=i, timestep=i) for i in x]\n",
"plt.plot(x,y)\n",
"plt.ylim([0,1])\n"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T05:06:39.621935Z",
"start_time": "2017-10-03T05:06:39.451201Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1)"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zPPTtY5PuiiQZ7uP0L9+wgb+de5ZTZ85OuiuSLnOG+xi9e9trOTF/lvv+/ulJd0XSZc5w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\nC7MbuAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f76133ec7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Before https://github.com/reinforceio/tensorforce/pull/150\n",
"# Currently there is a half life of 1 timestep\n",
"epsilon_timesteps = 1000\n",
"expl = explorations.EpsilonDecay(\n",
" epsilon=0.9,\n",
" epsilon_final=0.1,\n",
" epsilon_timesteps=epsilon_timesteps,\n",
" start_after=100)\n",
"x = np.linspace(0, epsilon_timesteps, 10000)\n",
"y = [expl(episode=i, timestep=i) for i in x]\n",
"plt.plot(x, y)\n",
"plt.ylim([0,1])"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T05:09:13.250556Z",
"start_time": "2017-10-03T05:09:13.025738Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.10094264155775015"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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vvXcRmXsU7tP0X69poaIowdcf3xV2KSIib6Jwn6bq0kI+cXULP92yn617j4ddjojIaRTu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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f76130c01d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# After https://github.com/reinforceio/tensorforce/pull/150\n",
"from tensorforce.core.explorations import Exploration\n",
"\n",
"class EpsilonDecay(Exploration):\n",
" \"\"\"\n",
" Exponentially decaying epsilon parameter based on ratio of\n",
" difference between current and final epsilon to total timesteps.\n",
" \"\"\"\n",
"\n",
" def __init__(self, epsilon=1.0, epsilon_final=0.1, epsilon_timesteps=10000, start_after=0, half_lives=10):\n",
" self.epsilon = epsilon\n",
" self.epsilon_final = epsilon_final\n",
" self.epsilon_timesteps = epsilon_timesteps\n",
" self.start_after = start_after\n",
" self.half_life = self.epsilon_timesteps / half_lives\n",
"\n",
" def __call__(self, episode=0, timestep=0):\n",
" if timestep < self.start_after:\n",
" return self.epsilon\n",
"\n",
" offset = self.start_after\n",
"\n",
" if timestep > self.epsilon_timesteps:\n",
" epsilon = self.epsilon_final\n",
" else:\n",
" epsilon = self.epsilon_final + \\\n",
" (self.epsilon - self.epsilon_final) * \\\n",
" 2 ** (- (timestep - offset) / self.half_life)\n",
"\n",
" return epsilon\n",
"\n",
"\n",
"epsilon_timesteps = 10000\n",
"expl = EpsilonDecay(\n",
" epsilon=1,\n",
" epsilon_final=0.1,\n",
" epsilon_timesteps=epsilon_timesteps,\n",
" start_after=100)\n",
"x = np.linspace(0, epsilon_timesteps * 2, 10000)\n",
"y = [expl(episode=i, timestep=i) for i in x]\n",
"plt.plot(x, y)\n",
"plt.ylim([0, 1])\n",
"expl(0, epsilon_timesteps - 1)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T04:30:54.227616Z",
"start_time": "2017-10-03T04:30:54.017874Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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gIjAnqswc4MbQ62uBt5xuMxQRaTAJu2Wcc+VmNgV4jeBQyMedc6vNbBqwxDk3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\nP59ZflHnjATSAAAAAElEQVQ1rwYeAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7615bcf518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# looks fine\n",
"epsilon_timesteps=100\n",
"expl = explorations.LinearDecay()\n",
"x=np.linspace(0,epsilon_timesteps,1000)\n",
"y=[expl(episode=i, timestep=i) for i in x]\n",
"plt.plot(x,y)\n",
"plt.ylim([0,1])"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"ExecuteTime": {
"end_time": "2017-10-03T05:21:17.426741Z",
"start_time": "2017-10-03T05:21:17.173966Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1)"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+w3Xb9tQqP8vZYXnHoSzIrJb3vQ6vL/CT2x620kR6sqzK1SjlGfU35JNoAkLk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\n6DAH0Sn3Dh06dJiD+P8BE9AsqGuH7tUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f76137d7b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# looks fine\n",
"epsilon_timesteps=1e8\n",
"expl = explorations.OrnsteinUhlenbeckProcess(sigma=0.3, mu=0, theta=0.15)\n",
"x=np.linspace(0,epsilon_timesteps,1000)\n",
"y=[np.clip(expl(episode=i, timestep=i),0,1) for i in x]\n",
"plt.plot(x,y)\n",
"plt.ylim([0,1])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "jupyter3",
"language": "python",
"name": "jupyter3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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