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distr_RL.ipynb
{
"cells": [
{
"source": [
"# Code for \"The optimist-pessimist ping-pong\"\n",
"\n",
"This IPython notebook was written to generate illustrations for this blog post: [https://modelingbehavior.netlify.app/posts/2020_distr_rl/](https://modelingbehavior.netlify.app/posts/2020_distr_rl/)\n",
"\n",
"It makes use of the class `DistrRLDemo` implemented in `distr_rl_demo.py` ([Github link](https://github.com/gmarkkula/blog/blob/main/content/posts/2020_distr_RL/distr_rl_demo.py)): "
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from distr_rl_demo import DistrRLDemo"
]
},
{
"source": [
"`DistrRLDemo` is a demo implementation of distributional reinforcement learning, as described in fuller detail in for example ([Dabney et al., 2019](https://doi.org/10.1038/s41586-019-1924-6); see also [this blog post](https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI)). The implementation in `DistrRLDemo` estimates quantiles (not expectiles), by using the learning rule:\n",
"\n",
"$$\n",
"V_i(t+1) = \\left\\{ \n",
" \\begin{array}\n",
" \\; V_i(t) + \\alpha_i^+ \\cdot \\mathrm{sign} [\\delta_i(t)] & \\mbox{if } \\delta_i(t) \\geq 0 \\\\\n",
" V_i(t) + \\alpha_i^- \\cdot \\mathrm{sign} [\\delta_i(t)] & \\mbox{if } \\delta_i(t) < 0 \\\\\n",
" \\end{array} \\right.\n",
"$$\n",
"\n",
"where $V_i(t)$ is the expectation of the $i$:th estimator at time step $t$, $\\delta(t)$ is the prediction error of the same estimator, compared to the observed value $V_{\\mathrm{obs}}(t)$ at the same time step:\n",
"\n",
"$$\n",
"\\delta_i(t) = V_{\\mathrm{obs}}(t) - V_i(t)\n",
"$$\n",
"\n",
"The values of the learning rates $\\alpha_i^+$ and $\\alpha_i^-$ for positive and negative prediction errors will cause estimator $i$ to converge to the quantile $\\tau_i$ of the distribution of $V_{\\mathrm{obs}}(t)$:\n",
"\n",
"$$\n",
"\\tau_i = \\frac{\\alpha_i^+}{\\alpha_i^+ + \\alpha_i^-}\n",
"$$\n",
"\n",
"`DistrRLDemo` can be used to estimate any set of quantiles of any number of distributions consisting of any number of gaussians, and it also has some methods for making a couple of different plots and corresponding animated GIFs. For example uses, see the code further below showing how the illustrations for the blog post were generated.\n",
"\n",
"Here is an interactive example with three gaussians in the distribution (`mu`s and `sigma`s are the means and standard deviations of the gaussians); click \"Run Interact\" after setting the sliders as you want them:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "interactive(children=(FloatSlider(value=1.0, description='mu1', max=20.0, min=-20.0), FloatSlider(value=5.0, d…",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "cb67750e6dd34dc4b6d747cb7965dc1b"
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<function __main__.run_rl_demo(mu1=1, mu2=5, mu3=20, sigma1=1, sigma2=2, sigma3=5, n_estimators=5, learn_rate=0.1, n_samples=1000, show_pdf=True, show_cdf=True, show_quantiles=True)>"
]
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"from ipywidgets import interact_manual\n",
"\n",
"def run_rl_demo(mu1 = 1, mu2 = 5, mu3 = 20, sigma1 = 1, sigma2 = 2, sigma3 = 5, \n",
" n_estimators = 5, learn_rate = 0.1, n_samples = 1000, \n",
" show_pdf = True, show_cdf = True, show_quantiles = True):\n",
" distr_RL = DistrRLDemo([(mu1, mu2, mu3)], [(sigma1, sigma2, sigma3)], \n",
" estimators = n_estimators, learn_rate = learn_rate, n_samples = n_samples)\n",
" distr_RL.set_plot_settings(plot_pdf = show_pdf, plot_cdf = show_cdf, \n",
" plot_quantiles = show_quantiles, plot_only_zero_xtick = False)\n",
" \n",
" fig = plt.figure(figsize = (5, 7))\n",
" axs = fig.subplots(2, 1, sharex = True)\n",
" distr_RL.plot_snapshot(axes = axs[0])\n",
" distr_RL.plot_estimator_trajectories(axes = axs[1])\n",
" axs[0].set_ylabel('Probability density')\n",
" axs[0].set_title('Observed distribution')\n",
" axs[1].set_xlabel('Observed (and estimated) quantity')\n",
" axs[1].set_ylabel('Time steps (= no of observations)')\n",
" axs[1].set_title('Estimator expectations')\n",
" plt.tight_layout()\n",
"\n",
"mu_slider = (-20, 20, 0.1)\n",
"sigma_slider = (0, 10, 0.1)\n",
"interact_manual(run_rl_demo, \n",
" mu1 = mu_slider, mu2 = mu_slider, mu3 = mu_slider, \n",
" sigma1 = sigma_slider, sigma2 = sigma_slider, sigma3 = sigma_slider, \n",
" n_estimators = (1, 10), learn_rate = (0.01, 1, 0.01), \n",
" n_samples = (100, 10000, 100), \n",
" show_pdf = True, show_cdf = False, show_quantiles = True)"
]
},
{
"source": [
"## Code for generating the figures in the blog post\n",
"\n",
"First just some constants defining the distributions used in the illustrations:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"\n",
"MUS_A = (1,)\n",
"SIGMAS_A = (2,) \n",
"COLOR_A = 'purple'\n",
"MUS_B = (0,)\n",
"SIGMAS_B = (2,) \n",
"COLOR_B = 'orange'\n",
"MUS_C = (-3, 5)\n",
"SIGMAS_C = (1.7, 1.7)\n",
"COLOR_C = 'green'"
]
},
{
"source": [
"And now a snippet of code for each figure / animation:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"distr_RL = DistrRLDemo([MUS_A], [SIGMAS_A], estimators=1, learn_rate=1, n_samples=1)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = False, plot_ests = False, \n",
" plot_xticks = True, plot_yticks = False, \n",
" plot_xlabel = 'Value of decision', plot_ylabel = 'Probability', distr_colors = (COLOR_A,))\n",
"fig = plt.figure(figsize=(5, 3))\n",
"ax = fig.subplots()\n",
"distr_RL.plot_snapshot(axes = ax)\n",
"TEXTY = -.015\n",
"plt.text(1 + 4*2 - 2, TEXTY, '+', verticalalignment = 'top', horizontalalignment = 'center')\n",
"plt.text(1 - 4*2 + 2, TEXTY, '–', verticalalignment = 'top', horizontalalignment = 'center')\n",
"plt.tight_layout()\n",
"plt.savefig('onegaussian.png')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"random.seed(0)\n",
"distr_RL = DistrRLDemo([MUS_A], [SIGMAS_A], estimators=1, learn_rate=1, n_samples=40)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = False, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A,))\n",
"#distr_RL.plot_snapshot()\n",
"distr_RL.save_gif('onegaussian.gif', rl_frame_step = 2, gif_frame_rate = 5)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"random.seed(0)\n",
"distr_RL = DistrRLDemo([MUS_A], [SIGMAS_A], estimators=1, learn_rate=0.1, n_samples=40)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = True, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A,))\n",
"#distr_RL.plot_snapshot()\n",
"distr_RL.save_gif('onegaussian_oneestimator.gif', gif_frame_rate = 2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"random.seed(2)\n",
"distr_RL = DistrRLDemo([MUS_A, MUS_B], [SIGMAS_A, SIGMAS_B], estimators=1, learn_rate=0.1, n_samples=40)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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"metadata": {
"needs_background": "light"
}
}
],
"source": [
"distr_RL.set_plot_settings(plot_pdf = False, plot_samples = True, plot_ests = False, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A, COLOR_B))\n",
"#distr_RL.plot_snapshot()\n",
"distr_RL.save_gif('twogaussians.gif', rl_frame_step = 2, gif_frame_rate = 5)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = True, \n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A, COLOR_B), \n",
" distr_labels = ('A', 'B'), distr_label_xs = (1 + 2*1.8, 0 - 2*1.8), distr_label_ys = (0.2, 0.2))\n",
"#distr_RL.plot_snapshot()\n",
"distr_RL.save_gif('twogaussians_oneestimator.gif', gif_frame_rate = 4)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"distr_RL = DistrRLDemo([MUS_A, MUS_B], [SIGMAS_A, (SIGMAS_B[0]/4,)], estimators=1, learn_rate=0.1, n_samples=1)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = False, plot_ests = False, \n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A, COLOR_B), \n",
" plot_quantiles = True, \n",
" distr_labels = ('A', 'B'), distr_label_xs = (1 + 2*1.7, 0 - 2*1.7/2), distr_label_ys = (0.15, 0.4))\n",
"distr_RL.plot_snapshot()\n",
"plt.savefig('twogaussians_diffvar.png')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"distr_RL = DistrRLDemo([MUS_A, MUS_C], [SIGMAS_A, SIGMAS_C], estimators=1, learn_rate=0.1, n_samples=1)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = False, plot_ests = False, \n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A, COLOR_C), \n",
" plot_quantiles = False, \n",
" distr_labels = ('A', 'C'), distr_label_xs = (1 + 2*1.15, -3 - 1.7*1.5), distr_label_ys = (0.8, 0.4))\n",
"distr_RL.plot_snapshot()\n",
"plt.savefig('oneandtwogaussians.png')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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c0eMKwgPbd/uD7LnZ2GpsJA9OJiIjol2v7Y2OLsPlLM9p9wRUb6/nh+wfmLlrJmX1ZU0eF2OO4eJuFzOuyzjNPtwI7UkC8lNbjmzhxu9uZEPBBgBig2K5tPulDEwaSGxQLHX2OrYXbed/W//H3rK9fLfrO+bvnc+t/W7l/I7noyhKu8Qp5bfTlzrMtRoub2Ue/W/v32694dblr+PNtW9SWlcKQGJIIoOTB5MRkUGQMYhaay37y/ez+tBqCmsKmb5xOrN2z+Ivg/9Cn4Q+7RKj8CySgPzQv9f/m8lzJ2N1WEkNS+WZEc9wXdZ1GPXG4449I/kM9pbu5dMtn7L+8HreXPsm6/LXcd+Q+zAbza2O5WTLhO31dnbP3g1AzyslATVXZMdIQhJCqC6opmh7EfFZ8W16PYfTwYcbP2Tm7pkAdI7qzA1ZN9A3oe8JP6jc2u9W1h1ex382/4cD5Qd4YskTXNHjCm7ofYOU5fyM/Gn7Eafq5MEfH+TO2XdidVi5rd9tbP/zdm7qe9MJk0+DTlGd+Pvwv3P/kPsJNgaz+tBq/rbob5TUte1ePPnr8rHV2kgZkkJEupTfmuvonVLbeouGens9U5ZNYebumRh0Bm7qcxMvnfcS/RL7NTlKVhSFQUmDeHX0q0zsNRGdouObHd/w4ooXsdgtbRqv8CySgPyEqqrcM+ce/rHqHxh0BqZfPJ33Ln6PEFNIs75fURTOyTiHf5z/D5JCk9hfvp/HFj9GflV+m8Xc8ES/lN9O39E7pbbVargaaw1P//w06w+vJ9QUyrPnPsvlPS5Hr9M36/v1Oj3X9LqGp855iiBjECvzVjJt+TSsDmubxCs8jyQgP3H/j/fz9rq3CdAHMPva2dzc7+YWnSc5LJkXz3uRDhEdyK/KZ8THIyiudf9Dj7Y6G/nrXMlNym+nL6JjBCEJIVgqLBRtK3L7+a0OKxf/72K2FW0jyhzFi+e9SGZcy1Yp9k3oywujXiA8IJwNBRt4fsXzOJwON0csPJEkID/w9tq3eXX1q5j0Jr675jvGdB7TqvOFBYTx7IhnyYjIYHfJbi753yXU2+vdFK3L4XWHcVgdpJ6VSnhq+6688wVtuVOqqqrcPftuFu9fTGRgJNNGTiM5LLlV50yPSOfZEc8SagplXf463l3/Lqqquili4akkAfm4hfsWMnnuZADeH/8+F3S+wC3nDQsI48lzniQ1LJWVuSu5fdbtbr1h5CzPAaT81hppQ9tmi4aXV73M9I3TMRvMPD78cZJCk9xy3oyIDJ4Y/gRGnZF5e+cxa7fsNeTrJAH5sH1l+5jw1QQcqoOHhz7MDX1ucOv5o83R/DDxB4KNwXy6+VPe//V9t5zXVmvj8PrDgJTfWiOiYwQhie4tw83fM5+HFjwEwCeXfUKXqC5uOW+D7jHduW/IfQB8uPFDdpWcekty4b0kAfkou9PO9TOup7y+nPFdxzNt1LQ2uU5WfBbvXPQOAJPnTmZjwcZWnzN/bT4Oq4OYnjGEJYe1+nz+yt1bNBTWFHLjdzeiovL0uU9zZc8rW33OExmWNozxXcfjUB28tOIlqq3VbXIdoT1JQD5qytIprMpbRXJoMh9d+lGbPl9xfe/rub3/7VgcFq766ipqrDWtOl/OClf5reHmKVqucTn2qpaV4WbtmsWsXbOYuXMm4/47jiM1R8iKy6J3XO823Y77pj430TmqM4W1hbz+y+syH+SjJAH5oBU5K3h26bMoKPznsv8QZW77Ddxeu+A1suKyyC7N5tFFj7b4PI3lNwVSz0x1Y4T+KaJDBKFJoa4y3NaWl+F+yP6B9YfXE2IK4b4h9zV7qXVLGfVGHjrrocbnzpYcWNKm1xPakATkY+rt9dz8/c04VScPDX2IER1GtMt1zUYzH1/6MQadgTfWvMGyg8tadJ5DvxzCaXMS2yMWc3TrOy34O3c8lFpQXcDHmz4G4J5B9xATFOO2+E4mISSB2/rdBsB7G96jrK7p3nLCO0kC8jFTl04luzSbnrE9eWbEM+167X6J/Xhk2COoqNwy8xZqbbWnfY6cZb+V34a7ym+7Zu066Zc4tcbl2C0ow6mqytvr3sbisHB22tmclXpWW4TYpJEdRtIvoR/V1mreXv+2lOJ8jCQgH7KtcBvPr3gegH9f9G9Nugw/PvxxesX1Yk/pHp75+fQSoKXKQsHGAhSdQupZUn5zl4iMlpfhlhxcwoaCDYSYQrit/21tFGHTFEXhnkH3YDaYWZ23mlV5q9o9BtF2JAH5CKfq5I7Zd2B32rlrwF0MTRuqSRwmvYkPLv4ABYVXVr3CruLmj1LyVuXhtDuJy4ojMCKwDaP0L0c/lNqwwKM5SutK+WDDBwDc0vcWIgO12Y02NjiWG/vcCMD7G953+0PPQjuSgHzEfzf/l5W5K0kISeC5857TNJbByYO5td+t2Jw2Js+d3OyySWP57WxZ/eZuDfNAeavyml2Ge2rJU1RaKsmKy2JUh1FtGd4pjek0ho6RHSmuLebr7V9rGotwH9mOwQfUWGv426K/AfD8qOeJCGx+5+i2Wkr73HnP8c2Ob1iwbwHf7vyWy3tcftLj68vrKdxSiM6gI+XMlDaJyZ9FZEQQmhxK1aEqCrcWktAn4aTHby/azr/W/gudouO2/re12/5PTdHr9Nw54E4eXvgwM3bOYFSHUSSGJmoak2g9GQH5gJdWvkR+VT4DEge4vdtBS8UExTBl5BQA7pt/3ynLJrkrc1GdKgl9EwgIDWiPEP3K6a6Gu//H+3GoDkZ3HE2HiA5tHV6z9IjpwYiMEdiddj7Y+IHW4Qg3kATk5fIq83hxxYsAvDrmVY/a0OvOAXeSFZdFTkUOb65586TH5iyV8ltba+gNl7fy5GW4OdlzmLdnHmEBYUzMmthe4TXLTX1uwmwws+bQGrYWbtU6HNFKnnO38hFHqo9w79x76fR6JwKmBJD8SjJj/zuWOdlz2uR6jyx6hDp7HRN6TuDs9LPb5BotpdfpefF8V3Kcumxq41bNf1RbXEvRjiL0Jj3JZ7Suq7JoWnhGOKHJoVgqLRRuLTzhMTaHjf+b/38A/H3430+rnNseIs2RXNb9MgA+2vSRLMv2cjIH5EYHyg8wdPpQQk2hPDfqOfrE98GpOlm0fxF3zb6LnPuavwKpOdYeWsunmz/FpDfxwnkvnPCYtmyX0hxjOo1hVIdRLNq/iOeWPcdLo1867pjcFbmgQuKARIxBTe/MKlqnoQy3/cvt5C7PPeE80Hu/vseukl10jurM5DMmM3/PfA0iPblLul3CnD1z2F2ym5V5K7m4+8VahyRaSEZAbjTph0moqsq6O9ZxVeZVdIvpRo/YHtwz+B423bXJ7dd7dLGr5c1fz/grHSI9o07/R4qiNCbH19e8zoHyA8cdc3DZQUDKb+2hsQx3gtVwtbZanl36LOBazKLFc2TNYTaaubbXtQD8Z/N/sDlsGkckWkoSkJuU1pUyb8887hl8zwm3uY40u/cZiiUHlrBw30LCAsJ4eNjDbj23uw1IGsDErIlYHVae+OmJY96rLqimdHcphkADSYPcs6+MaNoxZbjNx5bh3lrzFgXVBQxIHHDKVYtaO7/j+SSHJpNfle+2bUBE+5ME5CZ7SvegotIjpkebX0tVVR5b/BgAD571YLs0G22tKSOmYNKb+HTzp2w+srnx9YaN55IGJ2EIkIpwW1MUpXGkeXDpwcbXKy2VjV00poycovmy61Mx6Azc0Nu14vOpn59qdQd2oQ1JQG7SnpOhc7LnsDJ3JTFBMdx7xr3tdt3W6BDZgbsG3AXA0z8/3fh6w8On6WenaxKXP0of7vpd567MxVbnKl+9suoVSutKGZY2jDGdWrdle3s5M+VMukZ1pbCmkLfWvqV1OKIF5COnm3SJ7oKCwo7iHVzGZW12Hafq5PGfHgfg0WGPEhoQ2mbXao7TWeTQP7E/Jr2JGTtm8Nrq10gsSaR8fznGYCMJ/U/+YKRwn7CUMKI6R1G6p5TP3/0c/Uh941L+CztfyOzdszWOsHkUReHarGt5+ueneWnlS0waNOmE5W/huWQE5CZR5ijGdB7Dm2vePOEOjuX15W65zjfbv2FjwUZSwlK4e9Ddbjlne4kyRzG281gAPt/6OaXfu5Zlpw5NRW9s2/1lxLHSz3WNgkpnlfLNjm+os9fRL6EfmXGZGkd2evon9GdIyhCKa4t5a42MgryNjIDc6F/j/sVZ089i4L8H8uyIZ+kd3xsVlZ/2/8Rzy59r0TLso0cYTtXJgwseBODirhezYO8Ct8XeXi7vcTlz98zll0O/MGD1AOKII+PcDK3D8jtpZ6excfpG8n7N44fdPwCunW29jaIojO00ltV5q5m6bCoZERkEGYOOOWZ8t/EaRSdORUZAbtQhsgO/3vEr53c8n4cXPkzvd3oz8uORzNw9k3cverfV519zaA0HKw4SY47RvDlkS0UGRjKu8zgAFvZaSFBcELE9YzWOyv+YI83E94ln+ZDlWJ1WhqQMoUtUF63DapG+CX3pHt2dKmsVP2T/oHU44jTICMjNEkMTeWPcG7zBG249r6qqfLX9KwAu63EZRr33PrB5eY/LmbNjDru77cZutKPoPHvFla8KGxHGevN6gMbnarxRw1zQk0ue5Lud33FhlwuPGwUJzyQjIC+xsWAj2aXZhAeEM7rjaK3DaZUwwhi8bjAAP3SXT6xaWdBhAXajnW47u5FU7d3PYPWN70uPmB5UWau8ZhGFkBGQ1/hi+xcAXNr9UgIM3t0tuuKnCs5cciZr+q9hBSuYXjCdjMCMEx47MmJk+wbnYxaXLz7h69WOaj4r/QyA4UuHU5pQSuJd3ru9gaIoXNvrWv6+5O98t+s7Lup6kYyCvICMgLzA1sKtbC/aTogppHEVmTcr+b6E4NpgRiqu5DKvfJ7GEfmfnyp+wqJa6B3Qm+T8ZEpnlnp9Y88+8X3oGdOTams1c/fM1Toc0QySgLxAw9zP+K7jvf5Tna3URuXyStDDhBETMCpGNtdu5pD1kNah+Y06Zx1LKpcAMHHIRAxRBiz7LNRtqzvu2Lw1eSf98iSKojAhcwIA3+/6HovdonFE4lQkAXm47JJsNhRswGwwc1GXi7QOp9XK5pSBHcKGhRGbFMvQ0KEAzC/zvK7LvmpJxRLqnHV0DexKz8SeRI5z9Sks+b5E48har39CfzpFdqK8vpyF+xdqHY44BUlAHu7L7V8CMK7LOM27HrhD6UzXw6dR4139686LOA89etbXrKfQduI9aoT71DvrWVzhmhcaG+kq50ZfFg24/myc1qY3qvMGiqIwoadrFDRjxwzsTrvGEYmTkQTkwbYc2cIvh37BpDdxcTfv3/Okfl89tZtr0QXpiBjl2ugsyhDFkNAhqKjML5dRUFtbVrmMGmcNHQM60jWwKwDmnmbM3c04KhxULK7QOMLWG5IyhJSwFIpqi/j5wM9ahyNOQhKQB5u2fBrg2tQtMtC92zloofibYgAiL4xEZ/79r975EeejoPBL1S+U2Ly/DOSprE4rCytcZamxkWMbO14rikL05a5RUMk33v/71yk6ruxxJQBf7/gah9OhcUSiKZKAPNTukt18sfULDDpD4xbE3ky1qZR+5yq/NdzsGsQZ4xgYMhAnzsYbpHC/5VXLqXJUkRaQRk9zz2PeixofhWJUqFxeibXAqlGE7jM8fThxwXEcqjrEjB0ztA5HNEESkId6fvnzqKiMzBhJTFCM1uG0WsXSCuwldgI7BRLcN/i498dEuLYAWFG1ggq795eBPI1NtbGg3NU7cGzE2OP2+zFEGggfFQ4qjR8UvJlBZ+Dy7q5N9aYtn+b1S8x9lSQgD3Sg/AD/2fwf9IqeK3teqXU4blHytau0E3159Ak3O0syJdEnqA921c6iikXtHZ7PW121mgpHBUmmJLKCsk54TPQVv5XhZpT4xA37vI7nERkYycaCjfJckIeSBOSBXlzxInannWuzriUhxPv3ybEV2qhYWgEGiLqk6d1bG1ZlLatcRrXj+C0tRMs4VAc/lv8IuEY/OuXE/+zDzgrDGG/EkmOhep33//5NehOXdr8UgKnLpvpEUvU1koA8TH5VPtM3TEdB4ZFhj2gdjluUfF8CDgg/NxxjTNNNVBvmJiyqhWYnfmsAACAASURBVJ8qfmrHCH3b2uq1lNhLiDfG0y+4X5PHKXqFqEtdHxB8YTECwAWdLiAyMJKVuStZenCp1uGIP5AE5GFeXvkyFoeFy3tcTs/Ynqf+Bg+nqmrjzSzmilPPZV0QeQEASypdD0uK1nGqzsbl7WMixjQ5+mnQsECkfH45jmrvXz1mNpobt61vWFUqPIc0I21jp7NldaWlsnFv+2Fpw07rez1VzfoaLAcsGGONhJ0ddsrjOwd2pktgF7Lrs/m54mcujLqwHaL0XRtqNnDEdoRoQzSDQgYd9/6J2ukYuxmx7bKx7619mEeY2yPMNjX5jMn8Y9U/+HHvj6w9tJZBycf/HoQ2ZATkQWbumonFYWFg0kA6RXbSOhy3KPq8CICoy6NQDM3b9+eCCNcoaHHFYhkFtYJTdTK33DX5PjpiNHqledueB54bCEDd4jqfmDeJMkdx90DX9vXPLX9O42jE0SQBeYhqazWzs137mFzV8yqNo3EPW4mN8h/LQQcxVzV/KXl3c3fSA9KpdlbzVfFXbRihb1tSsYR8az7h+nCGhA5p9vcFDgpECVGwH7Rj3+cbrWz+78z/I0AfwLc7v2Vb4TatwxG/kQTkIeZkz6HWVkvvuN50j+mudThuUfJNCapNJfyccAKSm7+HkaIojI1wrYh7/8j7WJ3e/2Bke1NVlXcK3gFcnSaMSvN30FVMCubhrtJb3SLfGIEmhCRwa79bAdeKOOEZZA7IA9TZ6vh+1/cAje3kvZ3qUCn+n6v1Dv1PPNdwMr2CepFkSiLfms+0vGkMDxt+wuNkw7oTW1m1ks21mwnRhTR2HD8dgSMCqZ1TS/0v9YRcG4Iu1Ps/qz487GHe+/U9vtj2BU+d+xRdo7tqHZLf8/6/VT5g7p65VFmr6B7dnd5xvbUOxy0ql1Vizbeii9VhyjKd9vfrFB3jIsYB8GP5j9hV3ygFtZe3C94GYFTEKAJ0p7+DriHegKm3CWxQv7ze3eFpIi08jRv73IhTdTJtmayI8wSSgDRmsVv4due3AFzd6+oTdgnwRg2LD8wjzSi6lv1MfYP7kmhMpNReyuqq1e4Mz6etq17Huup1hOnDmhw5Nod55O9lONXp/YsRAB45+xH0ip5PN3/KvrJ9Wofj9yQBaWze3nlUWCroHNWZ/gn9tQ7HLSx5FiqXVh4zl9ASOkXX2B1hXvk8HKr3P5fSHhrmfq6PvR6zruW/f1NfE7poHY5CB9ZtvjEP1zGyI9f1vg6H6uD55c9rHY7fkwSkIavDyrc7XKOfazKv8a3RjwqRF0S2eu6gf3B/4o3xMgpqpi01W1heuZwgXRA3xN3QqnMpOqXxOaC6hb6xGAHg0WGPolN0fLTxI3IqcrQOx69JAtLQgn0LKK0vpUNEBwYl+cbDcY4aByVfujofxF4f2+rz6RQd4yJdc0EyCjq1Nw+/CcDE2IlEGlq/h5T5XDMYwbrRiv2wb8zDdYvpxtWZV2Nz2nhh+Qtah+PXJAFpxOaw8c32bwC4OtN35n5KZpTgqHIQPCCY4N7Hb7vQEgOCBxBvjKfEXsKa6jVuOacv2lSziZ8rfyZIF8Qtcbe45Zy6MB2BZwWCCnU/+s4o6LGzHwPggw0fkF+Vr3E0/ksSkEYW7V9EcV0x6eHpDElp/kOCnkx1qBR+UghA/E3xbjuvTtE1dkeYVyajoKa8cfgNwDX3E2Vsuuv46QoaEwRA3bI6nNVOt51XS5lxmVzR4wosDgsvrXhJ63D8liQgDdiddr7e8TUAE3pOOGWDSG9RsbgCa64VU6qJ8JHhbj33wJCBxBnjKLIXsbZ6rVvP7Qt+rf6V5ZXLCdYFc3P8zW49tyHltyXZVqj7yXdGQY8PfxyAd9e/S2FNocbR+Cd5EFUDSw4sobCmkOTQZIamnv5Dgp7qyEdHAIj7UxyK3r0lRb2i54KIC/ik6BPmlc9jUMigZvc28wcNo58b4250y9zPHwVdEIR1s5W6BXUEjQ1qdl8/T3Cypr6DkwazJn8NL698mRfOl/mg9uYbH729iN1p58vtXwKunm96nW/cRGs211CzvgZ9qL6xpb+7DQoZRKwhlkJboayIO8qaqjWsqlpFqD6UG+NubJNrGDON6FP0OMud1P/iGw+mgmv+FeCttW/JKEgDkoDa2cJ9CymoLiA5NJnh6S1/SNDTHPnQNfqJuToGfXDbJFW9oueiqIsA+KHsB2xOW5tcx5uoqsrrh18H4Ka4mwg3uLf02UBRFIIu+G0uaI5vdMkG6BLdhQu7XEiNrUaeC9KAJKB2ZHVY+XKba/QzMWuiz4x+6vfVUz6vHMWouGXp9ckMCB5AiimFckc5Sytlh8tfqn9hXfU6wvXh/CnuT216rcAzA9FF6LDn2rFu9I0HUwGmjJwCwL/W/ou8ytPrWShaRxJQO5q7Zy7FdcV0iOjgU3M/Be8WgArRl0VjSjj9vm+nQ6fouCTqEsD1XFCVo6pNr+fJVFXl5UMvA3Bz/M2E6kPb9HqKUSFonGsUVPN9jc+MgnIrchmaOhSLw8LtM29n1q5Zx3yJtiMJqJ3U2er4ertr5dv1Wdf7zMo3S66F0tmloIf4O9y39Ppkepp70jmwMzXOGj488mG7XNMTzSufx5baLcQaY/lTbNuOfhqYR5hRQhXs++zYtvlOCfS6rOvQKToW7FvA4arDWofjN3zjLugFZu2eRYWlgm7R3RiYNFDrcNym4N8F4ICo8VEEpJx+1+WWUBSlcRT0UeFHlNhK2uW6nsTqtPJq/qsA3JN4D0H6oHa5rhLw+1xQzfc17XLN9pASlsKIjBE4VAefb/1c63D8hiSgdlBtrW7seH1D7xt8puuBNd9K6XeloEDCnQnteu1OgZ3oFdSLWmdtY/NNf/Jl8ZfkWHLoGNCRK6KvaNdrm88zowQr2HbZqFrrOyXQazKvwaAz8PPBnzlYflDrcPyCJKB2MGPHDGpsNfSJ70PveN/Y7wfgyAdHUG0qkWMjCewQ2O7XvzjyYhQU/lf8P3Is/tNUstpRzb8K/gXA/yX/HwalfR/n05l1BI12jYIK3i5o12u3pfiQeEZ3Go2Kyn+3/lfrcPyCJKA2VlxbzMzdMwFXndlXWA9bKf7KteOpMlQhb03eCb/aUkpACpdEXYJNtfFi3ottei1P8sGRDyi1l9IvuB+jwkdpEoN5tBklUKFqZRXV66o1iaEtXNXzKkx6E6vzVrOjeIfW4fg8SUBt7NPNn2J1WDkr9Sy6x3TXOhy3OfzWYVSrSsAZARhStWuocV/SfQTpglhYsZBVlas0i6O95FnymH5kOgAPJj+oWTlXF6wjaKxrFHToH4d8ZkVclDmKS7tdCrgalfrKz+WpJAG1ofX561l8YDEGnYEbe7fNE+paqN9XT8mMEtBD8BXu6XjdUvGmeO5IuAOAaXnTfH7r7hfyXsCiWrgo8iL6h2i7gaH5AjOGKAM1G2uoWFShaSzudHmPy4kIjGB3yW6W5SzTOhyfJgmojaiqyv0/3g/ARV0uIjE0UeOI3Cf/tXxwQswVMRgStG8neHPczSSbksmuz+bL4i+1DqfNrKhcwYKKBQTpgngw+UGtw0Fn1pEwybX4JP/VfFS7b4wWgoxBXJ91PQCfbPqEervvtB7yNJKA2sjMXTP5+eDPhJpCuSrzKq3DcZuaLTWUzy9HCVBI+HP7rnxrSoAugIeSHwLg9fzXKbeXaxyR+9lUG9PypgFwV8JdxJtcz1wtLl980q+2FnNVDKZUE/V76yn53neWw4/qMIr08HQKawt5bfVrWofjsyQBtQGrw8qDC1yfUK/tdS0hphCNI3IPVVXJm+ZaWBB3Qxym+LbtenA6RkeMZnDIYMod5Y3dAXzJfwv/y976vaQHpHNT3E1ah9NIZ9KR9JckAA6/fhhnvW/sF6TX6bmln2tTv6nLpnKk+ojGEfkmSUBt4JVVr5Bdmk3X6K5c0PkCrcNxm7I5ZdRsqMEQbSDhLs8Y/TR80v+p4ifGRIxBj56vSr7i3cPvtssIoD0UWAsat1t4NOVRTDrPSfwAkRdGYu5hxnbE1tiU1hf0S+jHwMSBVFmreHjhw1qH45MkAbnZwfKDPPPzMwC8OfZNDDrt50jcwVnn5NA/DgGQ9Nck9CGe10g10ZTImIgxAHxW/Bk21ftbxaiqytO5T1PjrOG88PM4J/wcrUM6jqJTSPlbCuDqC2jN951Gpbf1v40AfQAfb/qYpQel+a27SQJys3vn3UudvY6rM6/m/E7nax2O2xyZfgTbYRvmHuY22+/HHcZEjiHeGM8R2xHml83XfI6kteaXz+enip8I0YXwROoTWofTpNAzQokYG4Far5L3ou90lE4KTeJvw/4GwKQfJmFzeP+HGk8iCciNZu+ezfe7vifUFMorY17ROhy3qT9Y7+r5BqQ8kuL23U7dyagYmRgzEXDdvA9bvbexZLm9nGdznwXggeQHGhceeKqUh1LQmXWUzyunarXvtOh5eOjDdIrsxLaibbz+y+tah+NTJAG5SZ2tjslzJwPw9LlPkxSapHFE7qGqKrlP56JaVKIuiSJ0cNu2/HeHLuYuDA0digMHHxd9jEN1aB3SaVNVlWdyn6HEXsLAkIFcFeP5KylNiSbi73QlyZxncnBanE12yGiPThnuYjaaeWOsaw7uySVPyp5BbiQJyE2e+OkJDpQfICsui8lnTNY6HLcpm11G1coq9OF6kh9O1jqcZrs8+nKiDFHkWHKYUzZH63BO2+yy2cwpm0OQLoipaVO9ZvuO+FviCegYgGWfhYJ3fKdP3NguY7m8x+XU2Gq4a/Zd0iHBTbzjb7WHW5m7kldWvYJe0fPBxR/4zMIDe5mdvOddn/aSH0rGGGXUOKLmM+vM3Bh7IwoK88rnsbd+r9YhNVu+NZ9ncl0LWR5JeYT0wHSNI2o+nUlH+rOueAveK8Ce6zudKV6/4HUiAiP4IfsHPtn0idbh+ATfuFNqqNZWy03f3YSKykNDH2JQ8iCtQ3Kb3GdzsZfYCRkYQvRlnrvwoCldzF04L/w8FlQs4KPCj3gk+ZF22zenpWyqjYcOPESVo4qR4SOJ0kd5xWKJo4UMCCFmYgzFnxVT+UElkU9EevS84akcvSvqTX1u4p+//JM/z/kzqqoSHRTN+G7jNYzOu8kIqJUeX/w42aXZZMZm8uQ5T2odjtuUzimlbE4ZuiAd6c+lo+i88wZyUdRFpJpSKbGX8HHRxzhVz35Q8tVDr7Kueh2xxlieTXvWa/eOSv6/ZIwJRuz77NTOqtU6HLcZkTGCQUmDqLHV8ObaN6UU10oyAmqFZQeX8c/V/0Sv6Pno0o8IMLTPjqBtzVZoI/fpXMBVegtI9d6fy6gYuT3+dp4/9DxbarfwY/mPXBDpmQ8H/1j2I9MLp2PAwGsdXiPa6H2jzgb6ED3p09LZc8sear6rwZRlwtjJe0q4TVEUhUmDJjF57mTWH17Pgn0LTvkhQUZITZMRUAuV1pVy3YzrUFH527C/+cw226pD5cDfDuCocBB2dhgxV8doHVKrxRhjGtvXzCqbxbbabdoGdAK763bzyMFHAHgw5UHNO127Q9hZYZjHmMEJle9U+kybnmhzNHf0d3Vgf+/X98ityNU4Iu8lCagFVFXl5u9vJrcylzOSz/Cp0lvBuwVUrazCEGUgbUqa15aA/qhXUC/GRYxDReX9I++Ta/Gcm8YR6xHu3HMnNc4axkWO40+xf9I6JLcJmRCCPlWP44iD6o+rfaZkdU76OZybfi4Wh4UXVr6AxW7ROiSvJAmoBd5Y8wYzd80kPCCc/135P4x67y8tAFStruLwm4dBgYwXMzyq2ag7XBh5IYNCBmFRLfyr4F+U2ku1DokaRw13772bw7bD9A3uy7T0aT6T9AEUk0L43eFggvoV9dT/5BtbGyiKwt0D7yY5NJmcihzeXf+u1iF5JZkDOk0rclbwwI8PAPDBxR+QEZGhbUBuYj1sZf8D+8EJCXcnEDYsDMBrHhZsDkVRuD72esrt5WTXZ/PG4Tc4J/wcYo2xmsRT56zjnn33sL1uO+kB6bzd6W0CdYGaxNKWDCkGwm4Jo/KdSqo+rcKQbvCJ+SCz0czDQx/mgQUPsHD/QrLishjRYYTWYXkVGQGdhrzKPK748gpsThv3nnEvV/S8QuuQ3MJR62DvpL3Yi+2EDgkl8c++s3neHxkVI3fG30mSKYkjtiPclH0Txbbido+jzlnHpL2TWFW1ilhDLO92epdIQ2S7x9FeAs8KxHyeGexQ8UYFjjLv605xIhkRGY3zQW+te4s9pXs0jsi7KKeoyfpGwdYN6u31DP9wOGvz1zKyw0jmXz+/WQ+cHv0MgRZONYJJHpjM/nv3U76gnID0ALp90Q1DxO8/ly+NgI5W5ajitcOvkW/Np1NgJ97r/B5JpvZpn1TlqGLyvsmsrlpNmD6Mvyb+lQSTZ2xv0ZZUu0r5c+XYsm0YMgxEPBpB2vA0rcNqNVVVeWPNGyzcv5AocxQvj36ZaHPzVzD6wSq5JmvKMgJqBofTwQ3f3sDa/LVkRGTwxZVf+ES3A1VVOfTSIcoXlKMP1dPp7U7HJB9fFqoP5d7Ee+kS2IW99Xu5eufVbKnZ0ubXzbPkMXHXRFZXrSbGEMO9iff6RfIBUAwK4X8NRx+nx37ATuXblagO7/+M2zAf1DO2J6V1pUxdNlUWJTSTjIBOYtauWaiqynu/vsfs7NkEG4N5btRzx8z7nOrTiyePgGpm1VDzVQ2KUaHT250a532a+/2+YEDIAP6y7y/8Uv0LgUogz6Q9w8XRF7vt/Ed3MdhVt4vphdOpclSRYExgUsIkYozev8z9dNkP2yl7pgy1RiX68mjXaksvfdD5aBX1Fdy/4H4KawoZmDSQR4c92qwPqjICEk36Zsc3zM6ejUFn4NFhj/rMooO6xXXUfFUDCqS/kH7C5OMPwg3hvNf5Pa6IvoJ6tZ6HDj7E/fvvp8Je4bZr2FQbM0pm8Prh16lyVNHd3J0Hkh7wy+QDYEg0EP5X18q4khkl5E3J84nl2eGB4Tw5/ElCTaGsy1/H67+87vGdN7QmCegkvtv5HZ9sdjUdvG/IfWTFZ2kckXvULqyl6iPXfi2pT6YSNS5K44i0ZdKZmJI2hWfTniVIF8QPZT9w0faL+Kr4q1Zt5aCqKptrNjMtbxoLKxaioDAuYhx/Tvizx/eka2umbiYi/hqBYlIo+qyIQ88f8okklBqeypPnPEmgIZAlB5fw7/X/liR0ElKCa8I/V/+T++bfB8A9g+5hdKfRJzxO6xLc6ZbIaufWUv15NQAh14UQNMa/b4QjI0Ye8/8P1h/k4YMPs7FmIwBdArtwU/xNjIsch1lnbtY5naqTpZVLef/I+6yrXgdAnDGOP8X+iY6BHd37A3i50JpQ9k7eC3aIvjKatKfTvLpxaYONBRt5Zukz2J12RncczaRBk5rcUsOfS3CSgP5AVVWe+fkZnvr5KQAmDZzEBZ2b7h3mLQlIdapUf1FN3dw6AEJvCsU8snk3VF/2xwQErgQyp2wOr+S/Qr41H3AtWjg/4nyGhg4lMyiT1IBU9IoeAIfqoNBWyLbabfxS9QsLyhdQYHPthROsC2Zs5FiGhw3HoPjHAo/TkTI4hYqlFez7yz7UepWIMRFkvJiBLsD7izO/Hv6VacunYXVYGZkxksmDJ6PX6Y87ThJQ0/wqATmcDv4858+8u/5ddIqOuwfezZhOY076Pd6QgFSLSuU7lVjWW0APobeFYh4qyQdOnIAaWJwWZpfN5suiL9lUu+mY9xQUwvSuebNaZy021XbM+8mmZCbGTiTeEO/35bbmsO6yUvFKBWqdiqGTgfB7w9FH6EkZnKJ1aK2y6cgmpiydgsVhYUDiAB4860GCjMf+fZAE1DS/SUDl9eVcN+M65mTPIUAfwOdXfO6WFSxaJyD7ETuVb1Riz7GjBCmE/yUcU0/farHTGidLQEfLrsvm54qfWVO9ht11uxtHOA2iDdF0DuxM/5D+nBt+LllBWegUndft5aMle46d8n+W4yx2oovUEf6XcDpc20HrsFptZ/FOpiybQqWlkg4RHXhi+BPEBP2+AMWfE5DUBIBthdu49ItL2VO6hyhzFN9e/S3D04drvoS6terX1lP1fhVqnYo+Tk/4feEYkuWP/HQcnUA6BnZsnMNxqA7qnHWcHX42Zp3ZJ1votDdDmoGop6KoeL0C224bZVPKCKoNIu7mOK9ept09pjsvnfcSTy99mv3l+/nr/L9y/5n30y+hn9ahac77C62toKoq7657l8HvD2ZP6R76JvRl3e3rGJ4+XOvQWsVZ46Ty3Uoq36hErVMJGBRA5DORknzcSK/oCdGHEGmIlOTjRrowHREPR2AebQYHHHrpEHtu34PlkHc/2JkYmsiL571I3/i+VFoqeWrJU3y25TPsTt/Zsrwl/LYEl1+Vz52z72T27tkA3ND7Bt656J1j6rPeMAI6ugSnqiqWtRaqP63GWe4EE4RcFYL5fLNPdVgW/sGywUL1h9U4yh3ognQk3ZdE7MRYr14l53A6+GLbF3yx7QtUVDpHdua7a74jMy5T69DakswBNXA4Hby19i0eX/w4VdYqIgIjePvCt7mm1zXHHetNCciea6fqv1XYtrsmw41djITeFoohUUY9wnvFd4gnd0ou5fPLAQjsGkjyg8mEDQvz6g9Vm45s4vVfXqeotgiT3sSDZz3II8MeIdgUrHVobUESkKqqzMmewyOLHmFLoavn1/iu43lr3Fukhqee8Hu8IQEd+PYANd/VYFljARWUYIWQCSEEnhvo1XVzIYDGVXDlC8rJey4Pa74VgNCzQkm6N4ngPt57w6611fLhxg+Zv3c+AClhKUwdOZWJWRN9otfkUfw3ATlVJ3Oz5/L8iudZnrMcgLTwNF6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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"random.seed(0)\n",
"distr_RL = DistrRLDemo([MUS_A, MUS_C], [SIGMAS_A, SIGMAS_C], estimators=1, learn_rate=0.1, n_samples=1000)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = True, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A, COLOR_C), \\\n",
" plot_quantiles = False, \n",
" distr_labels = ('A', 'C'), distr_label_xs = (1 + 2*1.15, -3 - 1.7*1.5), distr_label_ys = (0.8, 0.4))\n",
"#distr_RL.plot_estimator_trajectories()\n",
"distr_RL.save_gif('oneandtwogaussians_oneestimator.gif', rl_frame_step = 10, gif_frame_rate = 15)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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"source": [
"def plot_large_estimator(ax, x, y, l, r, c):\n",
" MARKER_SIZE = 20\n",
" LINE_WIDTH = 5\n",
" ax.plot(x, y, 'o', color = c, markersize = MARKER_SIZE)\n",
" ax.plot((x - l, x + r), (y, y), '-', color = c, lw = LINE_WIDTH)\n",
" ax.plot(x-l, y, '<', color = c, markersize = MARKER_SIZE)\n",
" ax.plot(x+r, y, '>', color = c, markersize = MARKER_SIZE)\n",
" ax.axis('off')\n",
" \n",
"fig = plt.figure(figsize = (4, 1))\n",
"ax = fig.subplots()\n",
"plot_large_estimator(ax, 0, 0, 1, 1, COLOR_A)\n",
"ax.set_xlim((-2, 2))\n",
"plt.savefig('averageestimator.png')\n",
"\n",
"fig = plt.figure(figsize = (4, 1))\n",
"ax = fig.subplots()\n",
"plot_large_estimator(ax, 0, 0, 1.5, 0.5, COLOR_A)\n",
"ax.set_xlim((-2, 2))\n",
"plt.savefig('pessimistestimator.png')\n",
"\n",
"fig = plt.figure(figsize = (4, 1))\n",
"ax = fig.subplots()\n",
"plot_large_estimator(ax, 0, 0, 0.5, 1.5, COLOR_A)\n",
"ax.set_xlim((-2, 2))\n",
"plt.savefig('optimistestimator.png')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"random.seed(0)\n",
"\n",
"XLIM = (-3-4*1.7, 5+4*1.7)\n",
"\n",
"distr_RL = DistrRLDemo([MUS_A], [SIGMAS_A], estimators=(.3,), learn_rate=0.2, n_samples=60)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = True, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A, COLOR_C), \\\n",
" plot_quantiles = False, lr_plot_scaling = 5, xlim = XLIM)\n",
"#distr_RL.plot_snapshot()\n",
"#distr_RL.plot_estimator_trajectories()\n",
"distr_RL.save_gif('onegaussian_onepessimistestimator.gif', gif_frame_rate = 6)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"random.seed(0)\n",
"distr_RL = DistrRLDemo([MUS_C], [SIGMAS_C], estimators=(.3,), learn_rate=0.2, n_samples=60)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = True, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_C), \\\n",
" plot_quantiles = False, lr_plot_scaling = 5, xlim = XLIM)\n",
"#distr_RL.plot_snapshot()\n",
"#distr_RL.plot_estimator_trajectories()\n",
"distr_RL.save_gif('twogaussians_onepessimistestimator.gif', gif_frame_rate = 6)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"random.seed(1)\n",
"distr_RL = DistrRLDemo([MUS_A], [SIGMAS_A], estimators=(.1, .3, .5, .7, .9), learn_rate=0.1, n_samples=150)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = True, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_A,), \\\n",
" plot_quantiles = True, lr_plot_scaling = 5, est_spacing = 0.01, xlim = XLIM)\n",
"#distr_RL.plot_snapshot()\n",
"#distr_RL.plot_estimator_trajectories()\n",
"distr_RL.save_gif('onegaussian_fiveestimators.gif', rl_frame_step = 2, gif_frame_rate = 10)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"random.seed(1)\n",
"distr_RL = DistrRLDemo([MUS_C], [SIGMAS_C], estimators=(.1, .3, .5, .7, .9), learn_rate=0.1, n_samples=150)\n",
"distr_RL.set_plot_settings(plot_pdf = True, plot_samples = True, plot_ests = True, \\\n",
" plot_xticks = True, plot_yticks = False, plot_xlabel = 'Value', distr_colors = (COLOR_C,), \\\n",
" plot_quantiles = True, lr_plot_scaling = 6.5, est_spacing = 0.005, xlim = XLIM)\n",
"#distr_RL.plot_snapshot()\n",
"#distr_RL.plot_estimator_trajectories()\n",
"distr_RL.save_gif('twogaussians_fiveestimators.gif', rl_frame_step = 2, gif_frame_rate = 10)"
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python3",
"display_name": "Python 3.7.6 64-bit",
"metadata": {
"interpreter": {
"hash": "99978ab45aa73d3ca376e4e00ef03164b3fc2740d62ae2b94f90ec9b26d04313"
}
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Raw
distr_rl_demo.py
import math
import random
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
from celluloid import Camera
FONTSIZE = 14
def adjust_lightness(color, amount):
"""
from https://stackoverflow.com/questions/37765197/darken-or-lighten-a-color-in-matplotlib
"""
import matplotlib.colors as mc
import colorsys
try:
c = mc.cnames[color]
except:
c = color
c = colorsys.rgb_to_hls(*mc.to_rgb(c))
return colorsys.hls_to_rgb(c[0], max(0, min(1, amount * c[1])), c[2])
class DistrRLDemo:
def get_sample(self, i_distribution):
i = random.randrange(self.n_gaussians[i_distribution])
return random.normalvariate(self.mus[i_distribution][i], self.sigmas[i_distribution][i])
def __init__(self, mus, sigmas, estimators, learn_rate, n_samples):
"""
mus and sigmas: lists with one item per distribution, each item a tuple defining
a number of peaks in the distribution.
estimators: either an int specifying the number of estimators, or a tuple of quantiles
learn_rate: average learning rate of estimators
n_samples: number of observations to draw from the distributions
"""
# init
# - set default plot settings
self.set_plot_settings()
# - store info about the distributions to learn from
# -- number of distributions
self.n_distributions = len(mus)
assert len(sigmas) == self.n_distributions
# -- peaks in each distribution
self.n_gaussians = []
self.mus = []
self.sigmas = []
for i_distr in range(self.n_distributions):
self.n_gaussians.append(len(mus[i_distr]))
assert len(sigmas[i_distr]) == self.n_gaussians[i_distr]
self.mus.append(np.array(mus[i_distr]))
self.sigmas.append(np.array(sigmas[i_distr]))
# - get the quantiles to estimate
if type(estimators) == tuple:
# user has provided a tuple of quantiles to use
self.n_estimators = len(estimators)
self.quantiles = np.array(estimators)
else:
assert type(estimators) == int
# user has specified the number of quantiles to use
self.n_estimators = estimators
self.quantiles = np.linspace(0, 1, self.n_estimators+2)[1:-1]
# - get the learning rates for the estimators (scaled so as to sum to
# 2 x learn_rate)
self.pos_learn_rates = 2 * learn_rate * self.quantiles
self.neg_learn_rates = 2 * learn_rate * (1 - self.quantiles)
# - more storing
self.n_samples = n_samples
self.n_anim_frames = 2 * n_samples
# run the distributional RL
# - prepare arrays for storing the results
self.estimator_expectations = \
np.full((self.n_distributions, self.n_samples, self.n_estimators), math.nan)
self.estimator_expectations[:, 0, :] = 0
self.prediction_errors = \
np.full((self.n_distributions, self.n_samples, self.n_estimators), math.nan)
self.samples = np.full((self.n_distributions, self.n_samples), math.nan)
# - loop through the requested number of learning time steps
for i in range(self.n_samples-1):
# loop through the distributions being learned from
for i_distr in range(self.n_distributions):
# draw a sample from the distribution
self.samples[i_distr, i] = self.get_sample(i_distr)
# calculate prediction errors for the estimators
self.prediction_errors[i_distr, i, :] = \
self.samples[i_distr, i] - self.estimator_expectations[i_distr, i, :]
# which estimators had positive/negative prediction errors?
idx_pos_errors = np.argwhere(self.prediction_errors[i_distr, i, :] >= 0)
idx_neg_errors = np.argwhere(self.prediction_errors[i_distr, i, :] < 0)
# update the estimator expectations
self.estimator_expectations[i_distr, i+1, idx_pos_errors] = \
self.estimator_expectations[i_distr, i, idx_pos_errors] \
+ self.pos_learn_rates[idx_pos_errors]
self.estimator_expectations[i_distr, i+1, idx_neg_errors] = \
self.estimator_expectations[i_distr, i, idx_neg_errors] \
- self.neg_learn_rates[idx_neg_errors]
def set_plot_settings(self,
plot_samples = True, plot_ests = True, plot_quantiles = False,
plot_pdf = False, plot_cdf = False,
plot_xticks = True, plot_only_zero_xtick = True, plot_yticks = True,
plot_xlabel = None, plot_ylabel = None,
distr_colors = None, distr_labels = None, distr_label_xs = None, distr_label_ys = None,
lr_plot_scaling = None, est_spacing = None, xlim = None):
self.plot_samples = plot_samples
self.plot_ests = plot_ests
self.plot_quantiles = plot_quantiles
self.plot_pdf = plot_pdf
self.plot_cdf = plot_cdf
self.plot_xticks = plot_xticks
self.plot_only_zero_xtick = plot_only_zero_xtick
self.plot_yticks = plot_yticks
self.plot_xlabel = plot_xlabel
self.plot_ylabel = plot_ylabel
self.distr_colors = distr_colors
self.distr_labels = distr_labels
self.distr_label_xs = distr_label_xs
self.distr_label_ys = distr_label_ys
self.lr_plot_scaling = lr_plot_scaling
self.est_spacing = est_spacing
self.xlim = xlim
def get_plot_xlims(self):
if self.xlim is None:
N_PLOT_STDDEVS = 4
plotmin_x = math.inf
plotmax_x = -math.inf
for i_distr in range(self.n_distributions):
this_plotmin_x = min(self.mus[i_distr] - N_PLOT_STDDEVS * self.sigmas[i_distr])
this_plotmax_x = max(self.mus[i_distr] + N_PLOT_STDDEVS * self.sigmas[i_distr])
plotmin_x = min(plotmin_x, this_plotmin_x)
plotmax_x = max(plotmax_x, this_plotmax_x)
else:
plotmin_x = self.xlim[0]
plotmax_x = self.xlim[1]
return (plotmin_x, plotmax_x)
def plot_snapshot(self, axes = None, n_data_samples = None, n_learned_samples = None):
# larger font size
plt.rc('font', size=FONTSIZE)
# need to create an axes for plotting?
if axes is None:
fig, axes = plt.subplots()
# plot colours provided?
if self.distr_colors is None:
self.distr_colors = []
for i_distr in range(self.n_distributions):
self.distr_colors.append('blue')
# plot the end result if no specific sample requested by user
if n_data_samples is None:
n_data_samples = self.n_samples
if n_learned_samples is None:
n_learned_samples = self.n_samples-1
# get a good x plot range
(plotmin_x, plotmax_x) = self.get_plot_xlims()
# loop over the distributions and plot
maxYs = [] # for keeping track of max Y output and to set the y limits accordingly
for i_distr in range(self.n_distributions):
base_color = self.distr_colors[i_distr]
dark_color = adjust_lightness(base_color, 0.9)
maxYs.append(-math.inf)
# plot the actual samples obtained?
if self.plot_samples:
N_BINS = 50
bin_width = (plotmax_x - plotmin_x) / N_BINS
unscaled_area = self.n_samples * bin_width
bin_counts, bin_edges = np.histogram(self.samples[i_distr, :n_data_samples], \
bins=N_BINS, range=(plotmin_x, plotmax_x))
sample_bar_heights = bin_counts / unscaled_area
axes.bar(bin_edges[:-1], sample_bar_heights, width=bin_width, align='edge', \
color=base_color, alpha=0.3)
# what will the sample bar heights be at the last sample?
final_bin_counts, __ = np.histogram(self.samples[i_distr, :], bins=N_BINS, \
range=(plotmin_x, plotmax_x))
final_sample_bar_heights = final_bin_counts / unscaled_area
maxYs[i_distr] = max(maxYs[i_distr], max(final_sample_bar_heights))
# get the true pdf and cdf for the distribution
distr_x = np.linspace(plotmin_x, plotmax_x, 500)
distr_pdf = np.zeros(distr_x.size)
for i in range(self.n_gaussians[i_distr]):
distr_pdf += scipy.stats.norm.pdf(distr_x, self.mus[i_distr][i], self.sigmas[i_distr][i]) \
/ self.n_gaussians[i_distr]
maxYs[i_distr] = max(maxYs[i_distr], max(distr_pdf))
distr_cdf = np.cumsum(np.diff(distr_x) * distr_pdf[1:])
# get (and possibly plot) the true locations of the sought quantiles for this distribution
true_quantiles = np.full(self.n_estimators, math.nan)
for i in range(self.n_estimators):
true_quantile_idx = np.argwhere(distr_cdf >= self.quantiles[i])[0]
true_quantiles[i] = distr_x[true_quantile_idx]
if self.plot_quantiles:
if self.plot_cdf:
axes.plot(np.array((plotmin_x, true_quantiles[i])), np.full(2, self.quantiles[i]), \
'--', color = base_color, lw = 1)
axes.plot(np.full(2, true_quantiles[i]), np.array((0, self.quantiles[i])), \
'--', color = base_color, lw = 1)
elif self.plot_pdf:
axes.plot(np.full(2, true_quantiles[i]), np.array((0, distr_pdf[true_quantile_idx])), \
'--', color = base_color, lw = 1)
else:
axes.plot(np.full(2, true_quantiles[i]), np.array((0, max(distr_pdf)/6)), \
'--', color = base_color, lw = 1)
# plot the true pdf and cdf?
if self.plot_pdf:
MIN_PLOT_PDF = 0.001
distr_pdf_hi = np.copy(distr_pdf)
distr_pdf_hi[np.argwhere(distr_pdf < MIN_PLOT_PDF)] = math.inf
axes.plot(distr_x, distr_pdf_hi, color = base_color, lw = 2)
if self.plot_cdf:
axes.plot(distr_x[:-1], distr_cdf, '--', color = base_color, lw = 1.5)
# y coords for estimator plotting
if self.est_spacing is None:
est_ys = np.zeros(self.n_estimators)
else:
est_ys = np.arange(self.n_estimators) * self.est_spacing
# plot the estimator expectations at the specified sample?
if self.plot_ests:
axes.plot(self.estimator_expectations[i_distr, n_learned_samples, :], \
est_ys, 'o', markersize=6, color=base_color)
# plot the learning rates per estimator?
if not self.lr_plot_scaling is None:
for i_est in range(self.n_estimators):
est_x = self.estimator_expectations[i_distr, n_learned_samples, i_est]
est_y = est_ys[i_est]
est_lrs = np.array((-self.neg_learn_rates[i_est], self.pos_learn_rates[i_est]))
axes.plot(est_x + self.lr_plot_scaling * est_lrs, np.full(2, est_y), '-', \
color=base_color, lw=1.5)
axes.plot(est_x + self.lr_plot_scaling * est_lrs[0], est_y, '<', \
color=base_color, markersize=4)
axes.plot(est_x + self.lr_plot_scaling * est_lrs[1], est_y, '>', \
color=base_color, markersize=4)
# set x limits
axes.set_xlim((plotmin_x, plotmax_x))
# set y limits
if self.plot_cdf:
max_y = 1
else:
max_y = max(maxYs) # max of pdfs and samples (if plotted)
ZOOM_FACTOR = 0.05
plotmin_y = -max_y * ZOOM_FACTOR
plotmax_y = max_y * (1 + ZOOM_FACTOR)
axes.set_ylim((plotmin_y, plotmax_y))
# distribution labels?
if not (self.distr_labels is None):
for i_distr, label in enumerate(self.distr_labels):
label_x = self.distr_label_xs[i_distr]
label_y = self.distr_label_ys[i_distr] * max_y
axes.text(label_x, label_y, label, color = self.distr_colors[i_distr],
fontweight = 'medium', horizontalalignment = 'center')
# axis ticks?
axes.spines['right'].set_visible(False)
axes.spines['top'].set_visible(False)
if not self.plot_xticks:
axes.tick_params(axis='x', bottom=False, labelbottom=False)
elif self.plot_only_zero_xtick:
axes.set_xticks((0,))
if not self.plot_yticks:
axes.tick_params(axis='y', left=False, labelleft=False)
# axis labels?
if not self.plot_xlabel is None:
axes.set_xlabel(self.plot_xlabel)
if not self.plot_ylabel is None:
axes.set_ylabel(self.plot_ylabel)
# axis lines?
if (self.plot_xlabel is None) and (not self.plot_xticks):
axes.spines['bottom'].set_visible(False)
if (self.plot_ylabel is None) and (not self.plot_yticks):
axes.spines['left'].set_visible(False)
plt.tight_layout()
def plot_estimator_trajectories(self, axes = None):
# init
plt.rc('font', size=FONTSIZE)
if axes is None:
fig, axes = plt.subplots()
for i_distr in range(self.n_distributions):
for i in range(self.n_estimators):
axes.plot(self.estimator_expectations[i_distr, :, i],
np.arange(self.n_samples), color = self.distr_colors[i_distr])
def get_n_samples_for_anim_frame(self, i_frame):
assert i_frame >= 0 and i_frame <= self.n_anim_frames-1
n_data_samples = math.ceil(i_frame/2)
n_learned_samples = math.floor(i_frame/2)
return (n_data_samples, n_learned_samples)
def save_gif(self, file_name, rl_frame_step = 1, gif_frame_rate=10):
fig, axes = plt.subplots()
camera = Camera(fig)
for i_frame in range(0, self.n_anim_frames, rl_frame_step):
n_data_samples, n_learned_samples = \
self.get_n_samples_for_anim_frame(i_frame)
self.plot_snapshot(axes=axes, n_data_samples=n_data_samples, n_learned_samples=n_learned_samples)
camera.snap()
animation = camera.animate()
animation.save(file_name, writer="imagemagick", fps=gif_frame_rate)
Raw
requirements.txt
matplotlib==3.2.1
numpy==1.18.5
celluloid==0.2.0
ipywidgets==7.5.1
scipy==1.4.1
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