readme.si.json

{
 "<h1><a href=\"https://nn.labml.ai/rl/dqn/index.html\">Deep Q Networks (DQN)</a></h1>\n<p>This is a <a href=\"https://pytorch.org\">PyTorch</a> implementation of paper  <a href=\"https://arxiv.org/abs/1312.5602\">Playing Atari with Deep Reinforcement Learning</a>  along with <a href=\"https://nn.labml.ai/rl/dqn/model.html\">Dueling Network</a>, <a href=\"https://nn.labml.ai/rl/dqn/replay_buffer.html\">Prioritized Replay</a>  and Double Q Network.</p>\n<p>Here is the <a href=\"https://nn.labml.ai/rl/dqn/experiment.html\">experiment</a> and <a href=\"https://nn.labml.ai/rl/dqn/model.html\">model</a> implementation.</p>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb\"><span translate=no>_^_0_^_</span></a> <a href=\"https://app.labml.ai/run/fe1ad986237511ec86e8b763a2d3f710\"><span translate=no>_^_1_^_</span></a> </p>\n": "<h1><a href=\"https://nn.labml.ai/rl/dqn/index.html\">\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 Q \u0da2\u0dcf\u0dbd (DQN)</a></h1>\n<p>\u0db8\u0dd9\u0dba <a href=\"https://pytorch.org\">PyTorch</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0d9a\u0dd2 \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 <a href=\"https://arxiv.org/abs/1312.5602\">\u0dc3\u0dd9\u0dbd\u0dca\u0dbd\u0db8\u0dca \u0d85\u0da7\u0dcf\u0dbb\u0dd2 \u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 \u0dc1\u0d9a\u0dca\u0dad\u0dd2\u0db8\u0dad\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0dda \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8</a> \u0dc3\u0dc4 <a href=\"https://nn.labml.ai/rl/dqn/model.html\">\u0da9\u0dd4\u0dbd\u0dd2\u0d82 \u0da2\u0dcf\u0dbd\u0dba</a> \u0dc3\u0db8\u0d9f, <a href=\"https://nn.labml.ai/rl/dqn/replay_buffer.html\">\u0db4\u0dca\u0dbb\u0db8\u0dd4\u0d9b\u0dad\u0dcf \u0db1\u0dd0\u0dc0\u0dad \u0db0\u0dcf\u0dc0\u0db1\u0dba</a> \u0dc3\u0dc4 \u0daf\u0dca\u0dc0\u0dd2\u0dad\u0dca\u0dc0 Q \u0da2\u0dcf\u0dbd\u0dba. </p>\n<p>\u0db8\u0dd9\u0db1\u0dca\u0db1 <a href=\"https://nn.labml.ai/rl/dqn/experiment.html\">\u0d85\u0dad\u0dca\u0dc4\u0daf\u0dcf</a> \u0db6\u0dd0\u0dbd\u0dd3\u0db8 \u0dc3\u0dc4 <a href=\"https://nn.labml.ai/rl/dqn/model.html\">\u0d86\u0daf\u0dbb\u0dca\u0dc1</a> \u0d9a\u0dca\u0dbb\u0dd2\u0dba\u0dcf\u0dad\u0dca\u0db8\u0d9a \u0d9a\u0dd2\u0dbb\u0dd3\u0db8. </p>\n<p><a href=\"https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb\"><span translate=no>_^_0_^_</span></a> <a href=\"https://app.labml.ai/run/fe1ad986237511ec86e8b763a2d3f710\"> <span translate=no>_^_1_^_</span></a> </p>\n",
 "Deep Q Networks (DQN)": "\u0d9c\u0dd0\u0db9\u0dd4\u0dbb\u0dd4 Q \u0da2\u0dcf\u0dbd (DQN)"
}