{
 "<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> </p>\n": "<h1><a href=\"https://nn.labml.ai/rl/dqn/index.html\">\u6df1\u5ea6 Q \u7f51\u7edc (DQN)</a></h1>\n<p>\u8fd9\u662f <a href=\"https://pytorch.org\">PyTorch</a> \u5b9e\u73b0\u7684 PyTorch <a href=\"https://arxiv.org/abs/1312.5602\">\u4f7f\u7528\u6df1\u5ea6\u5f3a\u5316\u5b66\u4e60\u73a9\u96c5</a>\u8fbe\u5229\u4ee5\u53ca<a href=\"https://nn.labml.ai/rl/dqn/model.html\">\u51b3\u6597\u7f51\u7edc</a>\u3001<a href=\"https://nn.labml.ai/rl/dqn/replay_buffer.html\">\u4f18\u5148\u56de\u653e</a>\u548c Double Q Network\u3002</p>\n<p>\u8fd9\u662f<a href=\"https://nn.labml.ai/rl/dqn/experiment.html\">\u5b9e\u9a8c</a>\u548c<a href=\"https://nn.labml.ai/rl/dqn/model.html\">\u6a21\u578b</a>\u5b9e\u73b0\u3002</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></p>\n",
 "Deep Q Networks (DQN)": "\u6df1\u5ea6\u95ee\u7b54\u7f51\u7edc (DQN)"
}