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20 | 20 | "outputs": [],
|
21 | 21 | "source": [
|
22 | 22 | "style.use(['dark_background', 'bmh'])\n",
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23 |
| - "%matplotlib notebook" |
| 23 | + "%matplotlib widget" |
24 | 24 | ]
|
25 | 25 | },
|
26 | 26 | {
|
|
163 | 163 | " ax.add_line(bar)\n",
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164 | 164 | "\n",
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165 | 165 | " car = Rectangle(\n",
|
166 |
| - " (x1, y1 - W / 2), L, W, 0, color='C2', alpha=0.8, transform=\n", |
| 166 | + " (x1, y1 - W / 2), L, W, color='C2', alpha=0.8, transform=\n", |
167 | 167 | " matplotlib.transforms.Affine2D().rotate_deg_around(x1, y1, θ0 * 180 / π) +\n",
|
168 | 168 | " ax.transData\n",
|
169 | 169 | " )\n",
|
|
191 | 191 | " \n",
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192 | 192 | " x, y = x - d * cos(θ1), y - d * sin(θ1) - W / 2\n",
|
193 | 193 | " trailer = Rectangle(\n",
|
194 |
| - " (x, y), d, W, 0, color='C0', alpha=0.8, transform=\n", |
| 194 | + " (x, y), d, W, color='C0', alpha=0.8, transform=\n", |
195 | 195 | " matplotlib.transforms.Affine2D().rotate_deg_around(x, y + W/2, θ1 * 180 / π) +\n",
|
196 | 196 | " ax.transData\n",
|
197 | 197 | " )\n",
|
|
252 | 252 | "episodes = 10\n",
|
253 | 253 | "inputs = list()\n",
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254 | 254 | "outputs = list()\n",
|
255 |
| - "# truck = Truck(); episodes = 10_000 # uncooment for creating the data set\n", |
| 255 | + "# truck = Truck(); episodes = 10_000 # uncomment for creating the data set\n", |
256 | 256 | "\n",
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257 | 257 | "for episode in tqdm(range(episodes)):\n",
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258 | 258 | " \n",
|
|
400 | 400 | ],
|
401 | 401 | "metadata": {
|
402 | 402 | "kernelspec": {
|
403 |
| - "display_name": "Python 3 [conda env:pDL]", |
| 403 | + "display_name": "Python 3 (ipykernel)", |
404 | 404 | "language": "python",
|
405 | 405 | "name": "python3"
|
406 | 406 | },
|
|
414 | 414 | "name": "python",
|
415 | 415 | "nbconvert_exporter": "python",
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416 | 416 | "pygments_lexer": "ipython3",
|
417 |
| - "version": "3.8.2" |
| 417 | + "version": "3.10.12" |
418 | 418 | }
|
419 | 419 | },
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420 | 420 | "nbformat": 4,
|
|
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