mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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462 lines
115 KiB
Plaintext
462 lines
115 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"name": "Counterfactual Regret Minimization (CFR) on Kuhn Poker",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.5"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AYV_dMVDxyc2"
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},
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"source": [
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"[](https://github.com/lab-ml/nn)\n",
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"[](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/cfr/kuhn/experiment.ipynb) \n",
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"\n",
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"## [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html) on Kuhn Poker\n",
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"\n",
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"This is an experiment learning to play Kuhn Poker with Counterfactual Regret Minimization CFR algorithm."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AahG_i2y5tY9"
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},
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"source": [
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"Install the `labml-nn` package"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "ZCzmCrAIVg0L"
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},
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"source": [
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"%%capture\n",
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"!pip install labml-nn wandb"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SE2VUQ6L5zxI"
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},
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"source": [
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"Imports"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "0hJXx_g0wS2C"
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},
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"source": [
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"from labml import experiment, analytics\n",
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"from labml_nn.cfr.analytics import plot_infosets\n",
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"from labml_nn.cfr.kuhn import Configs\n",
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"from labml_nn.cfr.infoset_saver import InfoSetSaver"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Lpggo0wM6qb-"
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},
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"source": [
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"Create an experiment, we only write tracking information to `sqlite` to speed things up.\n",
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"Since the algorithm iterates fast and we track data on each iteration, writing to\n",
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"other destinations such as Tensorboard can be relatively time consuming.\n",
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"SQLite is enough for our analytics."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "bFcr9k-l4cAg"
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},
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"source": [
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"experiment.create(name='kuhn_poker', writers={'sqlite'})"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-OnHLi626tJt"
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},
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"source": [
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"Initialize configurations"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Piz0c5f44hRo"
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},
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"source": [
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"conf = Configs()"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wwMzCqpD6vkL"
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},
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"source": [
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"Set experiment configurations and assign a configurations dictionary to override configurations"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 17
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},
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"id": "e6hmQhTw4nks",
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"outputId": "e20b5ea3-605b-4bcc-c9de-0da93b6c7f32"
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},
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"source": [
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"experiment.configs(conf, {'epochs': 1_000_000})"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\"></pre>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {
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"tags": []
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}
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EvI7MtgJ61w5"
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},
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"source": [
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"Set PyTorch models for loading and saving"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 68
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},
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"id": "GDlt7dp-5ALt",
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"outputId": "d72bab0a-f33d-4f3d-aa4c-3fb9d4443a41"
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},
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"source": [
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"experiment.add_model_savers({'info_sets': InfoSetSaver(conf.cfr.info_sets)})"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\">Prepare cfr...\n",
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" Prepare create_new_history<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t4.72ms</span>\n",
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"Prepare cfr<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t9.13ms</span>\n",
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"</pre>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {
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"tags": []
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}
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KJZRf8527GxL"
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},
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"source": [
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"Start the experiment and run the training loop."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 187
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},
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"id": "aIAWo7Fw5DR8",
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"outputId": "18cd2384-d6c0-42a3-feae-5a309563bb33"
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},
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"source": [
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"# Start the experiment\n",
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"with experiment.start():\n",
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" conf.cfr.iterate()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\">\n",
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"<strong><span style=\"text-decoration: underline\">kuhn_poker</span></strong>: <span style=\"color: #208FFB\">6f0ec266d28a11eb89600242ac1c0002</span>\n",
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"\t[dirty]: <strong><span style=\"color: #DDB62B\">\"\"</span></strong>\n",
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"Train<span style=\"color: #00A250\">...[DONE]</span><span style=\"color: #208FFB\">\t830,490.15ms</span>\n",
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"<span style=\"color: #60C6C8\"> A: </span><strong> 100.0%</strong>\n",
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"<span style=\"color: #60C6C8\">Ab: </span><strong> 100.0%</strong>\n",
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"<span style=\"color: #60C6C8\"> K: </span><strong> 0.0%</strong>\n",
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"<span style=\"color: #60C6C8\">Kb: </span><strong> 33.5%</strong>\n",
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"<span style=\"color: #60C6C8\"> Q: </span><strong> 33.2%</strong>\n",
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"<span style=\"color: #60C6C8\">Qb: </span><strong> 0.0%</strong>\n",
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"Total <span style=\"color: #208FFB\">6</span> item(s)</pre>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {
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"tags": []
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}
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "oBXXlP2b7XZO"
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},
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"source": [
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"inds = analytics.runs(experiment.get_uuid())"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "RJ0L4XH2Y8g4"
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},
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"source": [
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"# dir(inds)"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "htumVaMnY8g4",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 568
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},
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"outputId": "735a944d-1b96-49e8-97b6-64317ea515b1"
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},
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"source": [
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"plot_infosets(inds['average_strategy.*'], width=600, height=500).display()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"alt.Chart(...)"
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],
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"text/html": [
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"\n",
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"<div id=\"altair-viz-df24252eee3c4158b4abb8d1cfd11114\"></div>\n",
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"<script type=\"text/javascript\">\n",
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" (function(spec, embedOpt){\n",
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" let outputDiv = document.currentScript.previousElementSibling;\n",
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" if (outputDiv.id !== \"altair-viz-df24252eee3c4158b4abb8d1cfd11114\") {\n",
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" outputDiv = document.getElementById(\"altair-viz-df24252eee3c4158b4abb8d1cfd11114\");\n",
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" }\n",
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" const paths = {\n",
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" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
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" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
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" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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" };\n",
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"\n",
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" function loadScript(lib) {\n",
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" return new Promise(function(resolve, reject) {\n",
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" var s = document.createElement('script');\n",
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" s.src = paths[lib];\n",
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" s.async = true;\n",
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" s.onload = () => resolve(paths[lib]);\n",
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" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
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" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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" });\n",
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" }\n",
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"\n",
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" function showError(err) {\n",
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" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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" throw err;\n",
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" }\n",
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"\n",
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" function displayChart(vegaEmbed) {\n",
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" vegaEmbed(outputDiv, spec, embedOpt)\n",
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" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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" }\n",
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"\n",
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" if(typeof define === \"function\" && define.amd) {\n",
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" requirejs.config({paths});\n",
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" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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" } else if (typeof vegaEmbed === \"function\") {\n",
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" displayChart(vegaEmbed);\n",
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" } else {\n",
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" loadScript(\"vega\")\n",
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" .then(() => loadScript(\"vega-lite\"))\n",
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" .then(() => loadScript(\"vega-embed\"))\n",
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" .catch(showError)\n",
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" .then(() => displayChart(vegaEmbed));\n",
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" }\n",
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" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"values\": [{\"series\": \"A.b\", \"step\": 5001.5, \"value\": 0.9981992797118845}, {\"series\": \"A.b\", \"step\": 15000.0, \"value\": 0.9994003597841293}, {\"series\": \"A.b\", \"step\": 24999.0, \"value\": 0.9996379435191888}, {\"series\": \"A.b\", \"step\": 34998.0, \"value\": 0.9997420242497203}, {\"series\": \"A.b\", \"step\": 44997.0, \"value\": 0.999799438427597}, {\"series\": \"A.b\", \"step\": 54996.0, \"value\": 0.9998367791077256}, {\"series\": \"A.b\", \"step\": 64995.0, \"value\": 0.999862183020948}, {\"series\": \"A.b\", \"step\": 74994.0, \"value\": 0.9998803350618266}, {\"series\": \"A.b\", \"step\": 84993.0, \"value\": 0.9998942097468084}, {\"series\": \"A.b\", \"step\": 94992.0, \"value\": 0.9999053000410364}, {\"series\": \"A.b\", \"step\": 104991.0, \"value\": 0.9999141311503561}, {\"series\": \"A.b\", \"step\": 114990.0, \"value\": 0.9999214741911839}, {\"series\": 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