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Reinforcement Learning - FrozenLake Problem.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "authorship_tag": "ABX9TyMnN+bqS6OAnOvxCTxfynU8", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/jaroslawjanas/177ec5c648329f0c287351cd79587c7b/reinforcement-learning-frozenlake-problem.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# The FrozenLake Problem\n", | |
| "\n", | |
| "The *“deterministic”* FrozenLake is a toy problem from the so called *“grid world”* category of problems. In this problem the agent lives in a square grid and can move in 4 directions, “up”, “down”, “left” and “right”. The agent always starts in the top-left position and its goal is to reach the bottom right position on the grid (see image below)." | |
| ], | |
| "metadata": { | |
| "id": "rwNu_2ajQI0h" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "QHwvpd6qOkAS" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Just like the basic *Gridworld* actions are deterministic i.e. a move to the “right” will always move the agent to the cell directly to their right. The FrozenLake however does have holes in the ice and if the agent falls in, it will drown. Any action which causes a move off the grid results in the agent’s state remaining unchanged. " | |
| ], | |
| "metadata": { | |
| "id": "0rAX4vzvP8kO" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Part 1: Create the FrozenLake\n", | |
| "\n", | |
| "1. Using Python, create a 5x5 grid sized FrozenLake, with a start state at the top left corner and a goal state at the bottom right corner.\n", | |
| "2. Place four holes at the following grid positions in the FrozenLake. **(1,0)**, **(1,3)**, **(3,1)**, **(4,2)**\n", | |
| "3. The reward for reaching the goal state is **+10.0**. The reward for falling into a hole is **-5.0** (because you die!) and the rewards for each transition to a non-terminal state is **-1.0**.\n", | |
| "4. The episode ends if the agent falls into a hole or reaches the goal state.\n", | |
| "5. The actions are **“up”**, **“down”**, **“left”** and **“right”**.\n" | |
| ], | |
| "metadata": { | |
| "id": "tSjWuCQOQEmP" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "-vpFn3pBS0uf" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Imports\n", | |
| "import numpy as np\n", | |
| "import random\n", | |
| "from itertools import groupby\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "from scipy.interpolate import make_interp_spline" | |
| ], | |
| "metadata": { | |
| "id": "EqkSWCDJXG71" | |
| }, | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Board Size\n", | |
| "BOARD_ROWS = 5\n", | |
| "BOARD_COLS = 5" | |
| ], | |
| "metadata": { | |
| "id": "ljT5gaMUh8ca" | |
| }, | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Why is this solution good?\n", | |
| "\n", | |
| "* Note that you could easily pass in state `actions` and `q_actions` as arguments of *State*. This makes it possible to define per *State* actions.\n", | |
| "\n", | |
| "* It's easier to work with, for any state based application **OOP** is the king." | |
| ], | |
| "metadata": { | |
| "id": "z0KwI_JsvX3h" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 22, | |
| "metadata": { | |
| "id": "SERbkZfqRnlM" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# State\n", | |
| "class State:\n", | |
| " states = {}\n", | |
| " alpha = 0.5\n", | |
| " gamma = 0.9\n", | |
| " epsilon = 0.1\n", | |
| "\n", | |
| "\n", | |
| " def __init__(self, pos, reward=-1, is_end=False):\n", | |
| " self.pos = pos\n", | |
| " self.reward = reward\n", | |
| " self.is_end = is_end\n", | |
| " self.actions = [\"up\", \"down\", \"left\", \"right\"]\n", | |
| " self.q_actions = {\n", | |
| " \"up\": 0,\n", | |
| " \"down\": 0,\n", | |
| " \"left\": 0,\n", | |
| " \"right\": 0\n", | |
| " }\n", | |
| "\n", | |
| " self.states[self.pos] = self\n", | |
| "\n", | |
| "\n", | |
| " def move(self, action): \n", | |
| " if self.is_end:\n", | |
| " return None\n", | |
| "\n", | |
| " if action == \"up\": \n", | |
| " next_pos = (self.pos[0] - 1, self.pos[1]) \n", | |
| " elif action == \"down\":\n", | |
| " next_pos = (self.pos[0] + 1, self.pos[1])\n", | |
| " elif action == \"left\":\n", | |
| " next_pos = (self.pos[0], self.pos[1] - 1)\n", | |
| " else:\n", | |
| " next_pos = (self.pos[0], self.pos[1] + 1)\n", | |
| " \n", | |
| " if (next_pos[0] >= 0) and (next_pos[0] <= BOARD_COLS - 1):\n", | |
| " if (next_pos[1] >= 0) and (next_pos[1] <= BOARD_ROWS - 1): \n", | |
| " \n", | |
| " if next_pos in self.states:\n", | |
| " return self.states[next_pos] \n", | |
| " return State(next_pos)\n", | |
| "\n", | |
| " return self\n", | |
| "\n", | |
| "\n", | |
| " # https://stackoverflow.com/questions/3844801/\n", | |
| " # check-if-all-elements-in-a-list-are-identical\n", | |
| " @staticmethod\n", | |
| " def all_equal(iterable):\n", | |
| " g = groupby(iterable)\n", | |
| " return next(g, True) and not next(g, False)\n", | |
| "\n", | |
| "\n", | |
| " def best_action(self):\n", | |
| " if self.is_end:\n", | |
| " return (None, 0)\n", | |
| "\n", | |
| " if State.all_equal(self.q_actions):\n", | |
| " return (None, None)\n", | |
| "\n", | |
| " best_value = max(self.q_actions.values())\n", | |
| " best_action = max(self.q_actions, key=self.q_actions.get)\n", | |
| "\n", | |
| " return best_action, best_value\n", | |
| "\n", | |
| "\n", | |
| " def choose_action(self):\n", | |
| " if self.is_end:\n", | |
| " return None\n", | |
| "\n", | |
| " best_action, best_value = self.best_action()\n", | |
| "\n", | |
| " if random.uniform(0, 1) < self.epsilon:\n", | |
| " other_actions = \\\n", | |
| " [action for action in self.actions if action != best_action]\n", | |
| " return random.choice(other_actions)\n", | |
| "\n", | |
| " return best_action \n", | |
| "\n", | |
| "\n", | |
| " def update_q_action(self, action, next_state):\n", | |
| " # print(next_state.reward)\n", | |
| "\n", | |
| " self.q_actions[action] += \\\n", | |
| " self.alpha * (next_state.reward + \\\n", | |
| " (self.gamma * next_state.best_action()[1]) - \\\n", | |
| " self.q_actions[action])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Board Layout\n", | |
| "\n", | |
| "WIN_STATE = State((4, 4), reward=10, is_end=True)\n", | |
| "START_STATE = State((0, 0), reward=-1)\n", | |
| "HOLES_STATES = [\n", | |
| " State((1, 0), reward=-5, is_end=True),\n", | |
| " State((1, 3), reward=-5, is_end=True),\n", | |
| " State((3, 1), reward=-5, is_end=True),\n", | |
| " State((4, 2), reward=-5, is_end=True)\n", | |
| " ]" | |
| ], | |
| "metadata": { | |
| "id": "6buN6gMqh3RI" | |
| }, | |
| "execution_count": 23, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Part 2: Implement the Reinforcement Learning algorithm Q-learning\n", | |
| "\n", | |
| "1. Using the algorithmic steps outlined in the notes (Week 9 – Model Free Learning (Temporal Difference Learning)) (or S&B book), implement Q-learning on the FrozenLake problem to learn a policy which can navigate optimally through the lake.\n", | |
| "2. Set the parameters Alpha = **0.5**, Gamma = **0.9**, and Epsilon = **0.10**\n", | |
| "3. Run the frozen lake experiment for **10000** episodes and output the action value estimates at the end of the learning process.\n", | |
| "4. Plot a curve of the reward per episode (similar to what was depicted in the slides for the cliff walking task Q-learning vs SARSA)." | |
| ], | |
| "metadata": { | |
| "id": "PY4gH1aKRdMl" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Agent \n", | |
| "\n", | |
| "class Agent:\n", | |
| "\n", | |
| " def __init__(self):\n", | |
| " self.start_state = START_STATE\n", | |
| " \n", | |
| "\n", | |
| " def qlearn(self, episodes):\n", | |
| "\n", | |
| " data = {\n", | |
| " \"episodes\": [],\n", | |
| " \"rewards\": []\n", | |
| " }\n", | |
| "\n", | |
| " for episode in range(episodes):\n", | |
| " reward_sum = 0\n", | |
| " data[\"episodes\"].append(episode + 1)\n", | |
| "\n", | |
| " state = self.start_state\n", | |
| "\n", | |
| " while not state.is_end:\n", | |
| "\n", | |
| " action = state.choose_action()\n", | |
| "\n", | |
| " next_state = state.move(action)\n", | |
| " state.update_q_action(action, next_state)\n", | |
| " reward_sum += next_state.reward\n", | |
| "\n", | |
| " state = next_state\n", | |
| "\n", | |
| " data[\"rewards\"].append(reward_sum)\n", | |
| "\n", | |
| " return data \n", | |
| "\n", | |
| "\n", | |
| " def show_values(self):\n", | |
| " for i in range(BOARD_ROWS):\n", | |
| " print('-----------------------------------------')\n", | |
| " out = '| '\n", | |
| " for j in range(BOARD_COLS):\n", | |
| " state = State.states.get((i, j), None)\n", | |
| "\n", | |
| " best_value = 0\n", | |
| " if state:\n", | |
| " if state.is_end:\n", | |
| " out += \"END\".ljust(5) + ' | '\n", | |
| " continue\n", | |
| "\n", | |
| " best_choice, best_value = state.best_action()\n", | |
| "\n", | |
| " out += str(round(best_value, 2)).ljust(5) + ' | '\n", | |
| " print(out)\n", | |
| " print('-----------------------------------------')" | |
| ], | |
| "metadata": { | |
| "id": "FZWY5jHPXMaC" | |
| }, | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "agent = Agent()\n", | |
| "data = agent.qlearn(100000)\n", | |
| "print(agent.show_values())" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bzpnTFYzBn5R", | |
| "outputId": "3233b517-2fdb-4dd3-cf11-25cfb599a511" | |
| }, | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "-----------------------------------------\n", | |
| "| -0.43 | 0.63 | 1.81 | 3.12 | 4.58 | \n", | |
| "-----------------------------------------\n", | |
| "| END | 1.81 | 3.12 | END | 6.2 | \n", | |
| "-----------------------------------------\n", | |
| "| 1.81 | 3.12 | 4.58 | 6.2 | 8.0 | \n", | |
| "-----------------------------------------\n", | |
| "| 0.63 | END | 6.2 | 8.0 | 10.0 | \n", | |
| "-----------------------------------------\n", | |
| "| -0.53 | -0.5 | END | 10.0 | END | \n", | |
| "-----------------------------------------\n", | |
| "None\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "plt.plot(data[\"episodes\"],data[\"rewards\"])" | |
| ], | |
| "metadata": { | |
| "id": "9N_q9WxFBptB", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 447 | |
| }, | |
| "outputId": "f951580c-b19a-49a1-8f99-683754eb56d0" | |
| }, | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[<matplotlib.lines.Line2D at 0x7fe6f431c070>]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 26 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ], | |
| "image/png": 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pzgQ9d9UorZg8UC/NO1F5GU7PtJ+eVBxQHTedEXx7Ks4aoCnDu+ov14wOoHTzh92IHtla9ZNB+vOVPwwDPbRbe6UkxeupOSfo5gZtfO6qUSpr4jYJd0wpDaBdR9UfNfVY/PLM/po8NLDRhIN169kDAi47vbxbo9eWn9X8/MvPGqAX5wayPYNX0CFVd18wRGsvOf6Y6rln2lA9MvtoHccXZTWa/tOTerao3ozkRM//05wJ+q+LhuuBmcfJlZLYxFxH9evs8hpVNtg/jl+aF54+D8SEfnmSpBeuGaWLTigMeD8L9Ji5a+qQoNoTzq9E6seun43vpSQ/I+xG222TB+qmM/rphWtG6eYz+unWyQO51ifMgtoT5s+fr23btjX5KCoqanFjysrK9Mknn6impsZvGafTqYyMDK9HuAXyV1ygfn3uYPXtlKGfDCtQcW6aHr3kh6HVB3XJDKiO6eXdg17ulOFdFR/nUO/89KDn9eesIV00oN4Q+nXDnee7kjWtvLtWnD3QM82VmqhHmxhG/vRBgQ/vPbWsm6Yd3/jNPlgXHN8tLCfDzpkpOve4rkGVb6ikY/P79XnDu6pXXui2Z0MT+3f0GTaCMaFfvsp7+K/D33DvzWrwxjC6V47GluQGPPtp9fa3YL/qKM5NV0pifHAzhUjnzKN/3ZfkZ2jJaX197me+vqaYWhbY8dIngP2uvu5Z7SLyuc6VJ/XUhSO6R2BJwctISdT08u4qyc/QtPLuykhOtH4cl1hvflDvyDk5OcrJCd/XJps3b1b79u3ldDqbL9xK8GFudPk6Puu2iS3f3YdarJ+0QuFY19Hf9W1toOua1Bb2HURf2H5VtGfPHh08eFB79uzRkSNHtHnzZklScXGx0tLS9Oc//1kHDhzQ8ccfr+TkZFVWVuqWW27Rz372s3A1KeZx0IcfF1TDZoGcIyJ6GjnGhbXW8U5aw7k8lv9wC1twWbJkiR566CHP89LSo9cwvPLKKxozZowSExN15513au7cuTLGqLi4WKtWrdLs2bPD1aSQa8mGDef+HMs7GoBIi8y7Z3O/BG2LWmsgixVhCy4PPvhgk2O4TJw40WvgObSOlA7Yhk/hQif4O12HqSFRxjgu4RWbl2m3YpwiAfvF9IcMx/Cm2VqDhBTZdWMcl/AiuKARjjkgNsXioRnLGS5aYnE7tSYElwhjh0Yg2E/QUtHcd8J6DV8Y6w41/vgLL4LLMYgP8ReZx3pBV7THDmi09DA2p6Vd37CPmuqzSHWnr+W05CQdquZGez8KxLG2sP78oVzfWPiKIO77gyMS11k0XERL9tt4P30W6v0wkhfMxsJ+cCziHI6YDl8EFz8C2fFWTylVdlqSlvyor8/pl57Y/GB83Tqkami39hrdK0cje2Y3GvlzxeSBXs9Lu2b6retXPxnU7PKa88QVIxq91qV908P1XzyyUJ0zUxoNjPejgR3VPStV55f9MEjWST4GCXvwwuMavZbVLkmzRxX6XeacscXKz0jWT08q1qAGtzLolZemMwd30n/PGq4O7ZK8pq2YPFDZaU798syjI4n+9KSj9Vx9ck9dcHxXdctK1VmlnSUdHRCwX6cMDaw3yF4wLh55tP3lRVkq9zOQ28kluTpveONBxArq9fm904Z6/v/zU0vUyZWsy07s0WiesqIfRibulRv8wHRXn9xTOelO/eJ071GPF5/aRznpTl11UrHaJTU98Nolo5ve5+uP9Hrj6f00oke2euel64zBTQ9A2D7V+7hoar6pZY37Mzfde2yo4wo7qCQ/XT8a2NGznRry9YeJr36XpLNKOysn3akbftTX560N6lw5tlgdXb5vp3CVj5GzG96mw9dp6fIxP7QpziHdP2PY0fpO7qm8DKeuPrlloxPXmTSwo0ry03Vc96P7V/1j6u4LhirdmaCRxdka3r2DOtVbt+b2hToPXDhcWe2SVNFgtOj543spL8Opn43vJcl3WB1ZnN2gruMaHfN1inPTdO6wAhVl+7/lw6/O8X0O7dAuKejbUwzv7nuk8Iajik/s532T4dsmD9SAzi6N75vX5IjHvZsYfLLh8eLL/7vMe0DQ355fqt+cO1hZ7ZL0u+lDNbCzS/07Z3hGa44lDmP5b9ncbrdcLpeqq6tDOorukJsrdfCbQ5Kk3csn+S1njJHD4fD8JHDYL1/S/9Wb79eVH+j2dR96yt83fZjG9c1rVIckr3rqOBwOXfHwJj33XpVXW7ovfLZRWxq2s67M8UUdtLbeCL31571jSqmueuRtSdKuilPlcDj08yff0x827PGUGdTFpafmnKBfV36gO17e6XNZdf3gr3/qu/ihv+ulbZ95LbNhu+per+uPSXe8oa2fur2WXb/vCxc912jeujL1p+1ePqlRm+o/9zetYX/vqjjVq976Omem6H8WnuQ1/6wH/6512z9rVLbh9nzoouEa3TNbX/zrOw25uVKStHHxycpJO/rGW7e+9dtYN+9pgzrpjvMGe8o1nO6r3bsqTvX831fdDfuhbntc+ODf9eqOzxvVcd/ru7TsuW2N1q9hXfXbWH+ZvvZr6Wif7vvyW696/bW1rs7fvf6Rbnlue6P1Pn1QJ90xpbTRdven4f4jSb2vf141h2v9tscYo58/uUWPbNzTqB+MMRp044ty//uwz2nubw9r0E0vSpLWLzpJP7rjDc85ZfaoQi2e1PgPpfrt97dv1/XtVScVa+5/9NJxy9bpn197j1L+1wVj1M3HvZx8HRf1l9XweZ362/PcYQV69C3vW7SsveR4HV+U5al/5K0v65MvvvX0S/3lVjy3Tfe89pHX/H9beJL+deiIxq36qyRp57JTFB/n8GyzP14+Qmff9TdJR/9AGtM71+uc8MI1o7T7n9/ost//w7NMX8d63br995sf64antnhNf/7qUX5HHL7svzfphferGtVTvw2Vc0d73fut4XH43ifVOu23bzSq+/HLyj1h0peGx2zD81Vd/9Zfbv35Gv4/HFr6/h22n0O3FXUb1d/Gbfiyr2L1543mR4xNLdvhcDT53Yn/9ff1evPr27Bfm+q3hnU015/BlA9uvVpezlPezzyB7iPNLa+59Qt4ewSxzGDb0NJ6Gk5zNLOfhfq4C8U2Onqc/fDcmAAHnQtmP3XU3Vuu+Xr91dOSbdhUEX/HebD11t0zz9+0hnUe3UcCP2aC3Uvq/9Q+mPOMv3UIRnPvTf6mxcr7UVP4qghoJWLzFANfmvqYO0bfKyC2TawguIRZLIygeMxtCPnRavW3k2HFiTH8bN77YvUv4JZqXWuDSCG4hJjNJ0UgUIF+NRpNsdSWYxHJqxBj4Q+tcLF1zey+CjU8CC5ArOPE1fqwTUMi0uG0NQc7mxBcIqy1/BUYaW3lr466EyO7Cep4XWAavWYAMYPgAiBolo+igDYoFH80xtIfnm35ECS4wAqxdMKItDZ8fgKARggufrT0fbK5v0S7tE9tYc2hUzca59jeOU1OrzOix9FRX/v6GWSprSno0PRIwif66NfjCv0PFOVLWw5qvtTvU2dCZE9bdcdJwxGamzO4wP+Iyyf3OTqCdKGPUVyT4n9Yv3bOtjXUVrCfIhgjZdYbbbypw6ZTpu/jtluW9zk52BFymzKsW3DHvS+GP10aaVtHRZTdef4Q9WpimOZIuX/GMD373qc6Y1BnvfpB49FcJw/potpaozxXsj754ludM7SLJGlCvzytmDxQ/Tu1bAj81mBUz2yt/H5Y8NcWjNVrH36u678fSXN6eTf1zE3T2d/3V32zRhYqPTlBVdX/1sT++dp78Ftl+hiW+1gCS2s+vV0/qY+Kstup+tvv9JNhBRFd9m/OLdWf3tmnSQM6+pz+/NWjfL4+eWiBao00rFv7RtN++eMBGtqtvSY0GO5dkpIT4/VfFw3XkVrT6BYgre0il6B+3u2naFaaU/fPGCZnQrznPk31Z3n8snJ9/lWNeuT4vhVDn44ZWj2l1BNsHrxwuCqe264//uOTQJvg18wTuqudM0HlPXzf8iNYU4YX6JGNe5sv2Azb/zAiuETQpIG+T3yRlpXmbHRfofri4hw+75/jcDhC8qZh83ez047vpryMo/dj6ZqVqguyunmCS15Gsqb56dfE+DhNLevmed6vDYe/lkhNStDFowK7902ouVITmzxe/A33Hh/n0BQfx5EkpTkT/O4rkjS6l+9PQ+HbyX3830+nqWHx65w26Id7XmWnOTW9vJvP4BKsxPg4r3u1Hasfl3YJSXCxHV8VhVksJNtYaAMQaeEcrM3i7O3h82YcnCtgAYILEEN430BTWts4IrYFJdva21oRXEKsNfwlFos4YQBoi2z+aj1cCC4AAASgtXziZftaEFwAtEq2n5wB+EZwAYIQ9k9tebdFE0L5lSlfv8JWBBcAAAJB2IsJBJcQa3ghVSj282P9WWdTs4fzJ6OhXGZcG/vzsP536YFenBfJHrJhe1jQxIDU7+uQnE+amBbtC0Hj4/y3ztc+F67mRnLfaW4dvG6yGaJ22XD8NoXg4keotuv08u7qnJmi2aMKW1zHwoklyk136tqJvRtNG9cnT1ntknT7eYMbTVvyo77KTnPqxtP7+617fN88FeW000+GNR7tNVyun9RHOelO/fzUEq/XH7pouDq0S9L9M4Y1mmfF5IHKTnPql2f6Xxd/7ps+TB3aJemhi4a3uM1NmTWyUJ0zU3TB8d2aL+zHpIEdVZKf7hksKyMlQScUZ2l4YQflZTibnPfaib2Vm+7UggmN9w9JSk2KlyTdM22oJGnNzKP98eCFx7W4vTf8qK/PbVjfVSf3DLrecX1yNaiLS0tP6+t5ra7dwTpnWIG6dkjV9PKWb5dYcHe99T+WQfguHV2kzpkpmjmie6Npo3vlaFi39s3eziJYd18wRB3aJekPF5cFVH71lFJlpyXptskDG027eGShOrmSdfmYHhrVM1vDu3dQJ1eyz3om9MtT/84ZGtgl0+f0sb1zNLggUz1zfY+m26+TSwM6uzS+r/fAdqF8uz+pJFeDCzJV7GdEX19tKa1324khXTP9zuPLtO/PT86EOP33rMC2R6xyGMtv8+p2u+VyuVRdXa2MjBDeY+KXlfrn14ckSbuXTwp4vkE3vqjqb7/zms8Yc8yfbDSso/vCZyVJC08p0aWji/zWH8iyQ9G+YPlbZlNtaWpaXX9IvrfXsa5jXf33TBvqc5j2cGzjukMzkHqbW76vukPdXkm67/WP9Mtnt0kK7rjxVW8w6x9IG+u24WmDOmn1lNIW1ylJva5/XocO10pq+XoGIxR9UVdPXR1lt7ykA+4aSdKuilNDUn9Ty1z4x3e19u/eo74+fll5o5FtAzkHBNIfwR4TgUx//K29WvD/3vV67fmrR/kdPbk5gR6H9csdy74Qqv0oVFr6/s2Q/xEQip2kqTpaOi2YMqHmb5nHui7hmDdS9TesI5g6myt7LHW3dJnHWm8stzHSQrUeLTnuwrXMlpQPZt8I9pgIdnooBLqM+uVi+TwYKXxVBCBkWsuJEZFh297C/h0bCC4AAMAaBJcQs/ySIQAAYhrBBQCAAPBFUWwguABAGxQL993hkhG0BMEFAIAAELRiA8EFCAKXMAFAdBFcQoz3NSC2BTviqC//8f2Iqv5GXkVjg+uN+mqrwux2jV7LapcUhZa0bQxA5xefCQKtycvzT9SbHx0Mye0tlp81QMcXdtCE/o1HUYZv5wwrkJF0XPf2GrfqtWg3p0VKu7bX7ecNVtcOqfr20BF9c+iIcjN833YA4UNwARAysRz3i3LSVNTMfWEClZ6cqGnl3UNSV1sRH+fQlOFdo92MY3bG4M7RbkKbx1dFAIAoieWoi1hFcAEAANYguIQaV+cCABA2BBcAAGCNsAWX3bt3a9asWSosLFRKSop69OihpUuX6tChQ17l3n33XY0aNUrJyckqKCjQihUrwtUkAABgubD9qmj79u2qra3VPffco+LiYm3ZskWzZ8/WN998o5UrV0qS3G63xo8fr3Hjxunuu+/We++9p4suukiZmZm65JJLwtU0AABgqbAFl4kTJ2rixIme50VFRdqxY4fuuusuT3B5+OGHdejQIa1Zs0ZJSUnq16+fNm/erFWrVhFcAABAIxG9xqW6ulodOnTwPF+/fr1Gjx6tpKQfRh6cMGGCduzYoS+++MJnHTU1NXK73V6PWBIXF9mf9/FjQm8R7n40wL1c7BHPwQJLRSy47Ny5U6tXr9all17qea2qqkp5eXle5eqeV1VV+aynoqJCLpfL8ygoKAhLe1t6Ar5/xjBltUvS7ecNDml7GrrwhO7q0j5FU8rsH9ApFO6aOkRZ7ZL0+4vLwlL/WaWd1TM3TWNLcsJSf2txzrACde2Qqunl3aLdFDTjzu+PmZXnDIp2U4CgOIwJ7rZxCxcu1K233tpkmW3btqmkpMTzfN++fTrxxBM1ZswY3XfffZ7Xx48fr8LCQt1zzz2e17Zu3ap+/fpp69at6tOnT6O6a2pqVFNT43nudrtVUFCg6upqZWRkBLMqTTpu2Uv6/Kujy9m9fFJQ8xpj5IjAn56RWo4twt0f9Hdg6Cd7RGtbdV/4rCTpj5eP0NBu7SO+fMQGt9stl8sV9Pt30Ne4zJ8/XzNnzmyyTFFRkef/+/fv19ixYzVixAjde++9XuXy8/N14MABr9fqnufn+74HiNPplNPpDLbZERWpEwFvDt7C3R/0d2DoJ3tEe1uxq6Algg4uOTk5yskJ7OPyffv2aezYsRo6dKgeeOABxcV5fzNVXl6uxYsX67vvvlNiYqIkqbKyUr1791b79qRwAADgLWzXuOzbt09jxoxR165dtXLlSn3++eeqqqryunbl/PPPV1JSkmbNmqX3339fjz76qG6//XbNmzcvXM0CAMQIPnBBS4Tt59CVlZXauXOndu7cqS5dvG8jX3dZjcvl0osvvqg5c+Zo6NChys7O1pIlS/gpNAAA8ClswWXmzJnNXgsjSQMHDtTrr78ermYAAIBWhHsVAQAAaxBcAACANQguAADAGgQXAABgDYKLH/xMDwCA2ENwAQBERbRH7oWdCC4AAMAaBBcAAGANggsAALAGwQUAAFiD4AIAAKxBcAEAANYguAAAAGsQXAAAgDUSot2AWMW4SAAQHhP65Wn/l//WgM6uaDcFFiK4AAAi6p5pw2SMYeRctAhfFQEAIo7QgpYiuAAAAGsQXAAAgDUILgAAwBoEFwAAYA2CCwAAsAbBBQAAWIPgAgAArEFwAQAA1iC4+OEQgyMBABBrCC4AAMAaBBcAAGANggsAALAGwQUAAFiD4AIAAKxBcAEAANYguAAAAGsQXAAAgDUILgAAwBoEFz8cDJwLAEDMIbgAAABrEFwAAIA1CC4AAMAaBBcAAGANggsAALBG2ILL7t27NWvWLBUWFiolJUU9evTQ0qVLdejQIa8yDoej0ePNN98MV7MAAIDFEsJV8fbt21VbW6t77rlHxcXF2rJli2bPnq1vvvlGK1eu9Cr70ksvqV+/fp7nWVlZ4WoWAACwWNiCy8SJEzVx4kTP86KiIu3YsUN33XVXo+CSlZWl/Pz8cDUFAAC0EhG9xqW6ulodOnRo9Prpp5+u3NxcjRw5Uk8//XSTddTU1Mjtdns9AABA2xCx4LJz506tXr1al156qee1tLQ0/epXv9Ljjz+uZ599ViNHjtSZZ57ZZHipqKiQy+XyPAoKCiLRfAAAEAMcxhgTzAwLFy7Urbfe2mSZbdu2qaSkxPN83759OvHEEzVmzBjdd999Tc47ffp07dq1S6+//rrP6TU1NaqpqfE8d7vdKigoUHV1tTIyMoJYk6aNqFin/dX/liTtXj4pZPUCAICj798ulyvo9++gr3GZP3++Zs6c2WSZoqIiz//379+vsWPHasSIEbr33nubrb+srEyVlZV+pzudTjmdzoDbCwAAWo+gg0tOTo5ycnICKrtv3z6NHTtWQ4cO1QMPPKC4uOa/mdq8ebM6duwYbLMAAEAbELZfFe3bt09jxoxRt27dtHLlSn3++eeeaXW/IHrooYeUlJSk0tJSSdITTzyhNWvWNPt1EgAAaJvCFlwqKyu1c+dO7dy5U126dPGaVv+ymptvvlkff/yxEhISVFJSokcffVSTJ08OV7MAAIDFgr44N9a09OKe5nBxLgAA4dPS92/uVQQAAKxBcAEAANYguAAAAGsQXAAAgDUILn44HI5oNwEAADRAcAEAANYguAAAAGsQXAAAgDUILgAAwBoEFwAAYA2CCwAAsAbBBQAAWIPgAgAArEFwAQAA1iC4AAAAaxBcAACANQguAADAGgQXAABgDYILAACwBsEFAABYg+ACAACsQXABAADWILgAAABrEFwAAIA1CC4AAMAaBBc/HI5otwAAADREcAEAANYguAAAAGsQXAAAgDUILgAAwBoEFwAAYA2CCwAAsAbBBQAAWIPgAgAArEFwAQAA1iC4+DGmd44kKSfdGeWWAACAOgnRbkCs+vmpfdQ7P0Pj+uRGuykAAOB7BBc/UpMSNO34btFuBgAAqIevigAAgDUILgAAwBoEFwAAYA2CCwAAsEZYg8vpp5+url27Kjk5WR07dtS0adO0f/9+rzLvvvuuRo0apeTkZBUUFGjFihXhbBIAALBYWIPL2LFj9dhjj2nHjh364x//qP/93//V5MmTPdPdbrfGjx+vbt26adOmTbrtttv0i1/8Qvfee284mwUAACzlMMaYSC3s6aef1plnnqmamholJibqrrvu0uLFi1VVVaWkpCRJ0sKFC/XUU09p+/btAdXpdrvlcrlUXV2tjIyMcDYfAACESEvfvyN2jcvBgwf18MMPa8SIEUpMTJQkrV+/XqNHj/aEFkmaMGGCduzYoS+++MJnPTU1NXK73V4PAADQNoQ9uFx33XVq166dsrKytGfPHv3pT3/yTKuqqlJeXp5X+brnVVVVPuurqKiQy+XyPAoKCsLXeAAAEFOCDi4LFy6Uw+Fo8lH/a54FCxbo7bff1osvvqj4+HhNnz5dx/Lt1KJFi1RdXe157N27t8V1AQAAuwQ95P/8+fM1c+bMJssUFRV5/p+dna3s7Gz16tVLffr0UUFBgd58802Vl5crPz9fBw4c8Jq37nl+fr7Pup1Op5xObnwIAEBbFHRwycnJUU5OTosWVltbK+nodSqSVF5ersWLF+u7777zXPdSWVmp3r17q3379i1aBgAAaL3Cdo3Lhg0b9Nvf/labN2/Wxx9/rJdffllTpkxRjx49VF5eLkk6//zzlZSUpFmzZun999/Xo48+qttvv13z5s0LV7MAAIDFwnZ36NTUVD3xxBNaunSpvvnmG3Xs2FETJ07U9ddf7/mqx+Vy6cUXX9ScOXM0dOhQZWdna8mSJbrkkksCXk7d9TL8uggAAHvUvW8He91rRMdxCYdPPvmEXxYBAGCpvXv3qkuXLgGXtz641NbWav/+/UpPT5fD4Qhp3W63WwUFBdq7dy+D24UR/RwZ9HNk0M+RQT9HRjj72Rijr776Sp06dVJcXOBXroTtq6JIiYuLCyqptURGRgYHRgTQz5FBP0cG/RwZ9HNkhKufXS5X0PNwd2gAAGANggsAALAGwaUJTqdTS5cuZcC7MKOfI4N+jgz6OTLo58iIxX62/uJcAADQdvCJCwAAsAbBBQAAWIPgAgAArEFwAQAA1iC4+HHnnXeqe/fuSk5OVllZmTZu3BjtJsWMiooKHXfccUpPT1dubq7OPPNM7dixw6vMv//9b82ZM0dZWVlKS0vT2WefrQMHDniV2bNnjyZNmqTU1FTl5uZqwYIFOnz4sFeZV199VUOGDJHT6VRxcbEefPDBRu1pK9tq+fLlcjgcuuaaazyv0c+hsW/fPl1wwQXKyspSSkqKBgwYoLfeessz3RijJUuWqGPHjkpJSdG4ceP04YcfetVx8OBBTZ06VRkZGcrMzNSsWbP09ddfe5V59913NWrUKCUnJ6ugoEArVqxo1JbHH39cJSUlSk5O1oABA/Tcc8+FZ6Uj7MiRI7rhhhtUWFiolJQU9ejRQzfffLPXfWro55Z57bXXdNppp6lTp05yOBx66qmnvKbHUr8G0pZmGTSydu1ak5SUZNasWWPef/99M3v2bJOZmWkOHDgQ7abFhAkTJpgHHnjAbNmyxWzevNmceuqppmvXrubrr7/2lLnssstMQUGBWbdunXnrrbfM8ccfb0aMGOGZfvjwYdO/f38zbtw48/bbb5vnnnvOZGdnm0WLFnnKfPTRRyY1NdXMmzfPbN261axevdrEx8ebF154wVOmrWyrjRs3mu7du5uBAweaq6++2vM6/XzsDh48aLp162ZmzpxpNmzYYD766CPzl7/8xezcudNTZvny5cblcpmnnnrKvPPOO+b00083hYWF5ttvv/WUmThxohk0aJB58803zeuvv26Ki4vNlClTPNOrq6tNXl6emTp1qtmyZYt55JFHTEpKirnnnns8Zf7nf/7HxMfHmxUrVpitW7ea66+/3iQmJpr33nsvMp0RRsuWLTNZWVnmmWeeMbt27TKPP/64SUtLM7fffrunDP3cMs8995xZvHixeeKJJ4wk8+STT3pNj6V+DaQtzSG4+DB8+HAzZ84cz/MjR46YTp06mYqKiii2KnZ99tlnRpL561//aowx5ssvvzSJiYnm8ccf95TZtm2bkWTWr19vjDl6oMXFxZmqqipPmbvuustkZGSYmpoaY4wx1157renXr5/Xss4991wzYcIEz/O2sK2++uor07NnT1NZWWlOPPFET3Chn0PjuuuuMyNHjvQ7vba21uTn55vbbrvN89qXX35pnE6neeSRR4wxxmzdutVIMn//+989ZZ5//nnjcDjMvn37jDHG/Od//qdp3769p9/rlt27d2/P85/85Cdm0qRJXssvKyszl1566bGtZAyYNGmSueiii7xeO+uss8zUqVONMfRzqDQMLrHUr4G0JRB8VdTAoUOHtGnTJo0bN87zWlxcnMaNG6f169dHsWWxq7q6WpLUoUMHSdKmTZv03XffefVhSUmJunbt6unD9evXa8CAAcrLy/OUmTBhgtxut95//31Pmfp11JWpq6OtbKs5c+Zo0qRJjfqCfg6Np59+WsOGDdM555yj3NxclZaW6ne/+51n+q5du1RVVeW1/i6XS2VlZV79nJmZqWHDhnnKjBs3TnFxcdqwYYOnzOjRo5WUlOQpM2HCBO3YsUNffPGFp0xT28JmI0aM0Lp16/TBBx9Ikt555x298cYbOuWUUyTRz+ESS/0aSFsCQXBp4J///KeOHDnidaKXpLy8PFVVVUWpVbGrtrZW11xzjU444QT1799fklRVVaWkpCRlZmZ6la3fh1VVVT77uG5aU2Xcbre+/fbbNrGt1q5dq3/84x+qqKhoNI1+Do2PPvpId911l3r27Km//OUvuvzyy3XVVVfpoYcekvRDPzW1/lVVVcrNzfWanpCQoA4dOoRkW7SGfl64cKHOO+88lZSUKDExUaWlpbrmmms0depUSfRzuMRSvwbSlkBYf3doRNecOXO0ZcsWvfHGG9FuSquzd+9eXX311aqsrFRycnK0m9Nq1dbWatiwYbrlllskSaWlpdqyZYvuvvtuzZgxI8qtaz0ee+wxPfzww/rDH/6gfv36afPmzbrmmmvUqVMn+hlB4ROXBrKzsxUfH9/olxkHDhxQfn5+lFoVm6688ko988wzeuWVV9SlSxfP6/n5+Tp06JC+/PJLr/L1+zA/P99nH9dNa6pMRkaGUlJSWv222rRpkz777DMNGTJECQkJSkhI0F//+lfdcccdSkhIUF5eHv0cAh07dlTfvn29XuvTp4/27Nkj6Yd+amr98/Pz9dlnn3lNP3z4sA4ePBiSbdEa+nnBggWeT10GDBigadOmae7cuZ5PE+nn8Iilfg2kLYEguDSQlJSkoUOHat26dZ7XamtrtW7dOpWXl0exZbHDGKMrr7xSTz75pF5++WUVFhZ6TR86dKgSExO9+nDHjh3as2ePpw/Ly8v13nvveR0slZWVysjI8LyJlJeXe9VRV6aujta+rU4++WS999572rx5s+cxbNgwTZ061fN/+vnYnXDCCY1+zv/BBx+oW7dukqTCwkLl5+d7rb/b7daGDRu8+vnLL7/Upk2bPGVefvll1dbWqqyszFPmtdde03fffecpU1lZqd69e6t9+/aeMk1tC5v961//Ulyc91tOfHy8amtrJdHP4RJL/RpIWwIS8GW8bcjatWuN0+k0Dz74oNm6dau55JJLTGZmptcvM9qyyy+/3LhcLvPqq6+aTz/91PP417/+5Slz2WWXma5du5qXX37ZvPXWW6a8vNyUl5d7ptf9THf8+PFm8+bN5oUXXjA5OTk+f6a7YMECs23bNnPnnXf6/JluW9pW9X9VZAz9HAobN240CQkJZtmyZebDDz80Dz/8sElNTTW///3vPWWWL19uMjMzzZ/+9Cfz7rvvmjPOOMPnz0lLS0vNhg0bzBtvvGF69uzp9XPSL7/80uTl5Zlp06aZLVu2mLVr15rU1NRGPydNSEgwK1euNNu2bTNLly61+me69c2YMcN07tzZ83PoJ554wmRnZ5trr73WU4Z+bpmvvvrKvP322+btt982ksyqVavM22+/bT7++GNjTGz1ayBtaQ7BxY/Vq1ebrl27mqSkJDN8+HDz5ptvRrtJMUOSz8cDDzzgKfPtt9+aK664wrRv396kpqaaH//4x+bTTz/1qmf37t3mlFNOMSkpKSY7O9vMnz/ffPfdd15lXnnlFTN48GCTlJRkioqKvJZRpy1tq4bBhX4OjT//+c+mf//+xul0mpKSEnPvvfd6Ta+trTU33HCDycvLM06n05x88slmx44dXmX+7//+z0yZMsWkpaWZjIwMc+GFF5qvvvrKq8w777xjRo4caZxOp+ncubNZvnx5o7Y89thjplevXiYpKcn069fPPPvss6Ff4Shwu93m6quvNl27djXJycmmqKjILF682OvntfRzy7zyyis+z8kzZswwxsRWvwbSluY4jKk3bCEAAEAM4xoXAABgDYILAACwBsEFAABYg+ACAACsQXABAADWILgAAABrEFwAAIA1CC4AAMAaBBcAAGANggsAALAGwQUAAFiD4AIAAKzx/wH2LVmI4NvWsAAAAABJRU5ErkJggg==\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Smoothed\n", | |
| "# https://www.geeksforgeeks.org/how-to-plot-a-smooth-curve-in-matplotlib/\n", | |
| "spline = make_interp_spline(data[\"episodes\"],data[\"rewards\"])\n", | |
| "\n", | |
| "x = np.linspace(min(data[\"episodes\"]), max(data[\"episodes\"]), 50)\n", | |
| "y = spline(x)\n", | |
| "\n", | |
| "plt.plot(x, y)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 447 | |
| }, | |
| "id": "J53mi64-z_wT", | |
| "outputId": "de816835-115f-404d-b310-1ce4a924f01b" | |
| }, | |
| "execution_count": 27, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[<matplotlib.lines.Line2D at 0x7fe6f4474430>]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 27 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "aZd9XTsi1cia" | |
| }, | |
| "execution_count": 27, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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