Notion has a lot of keyboard shortcuts, so it would be impractical to list them all here. If you’ve used shortcuts in Evernote, Microsoft Word, or Google Docs, then you probably know most of these already. You can also use classic text formatting shortcuts in Notion. You can also view this table directly inside Notion. Some (such as footnotes) are simply not included, while others (like image embedding) are achieved using other tools like the slash command.įor a full breakdown of Notion’s Markdown support, see Markdown Guide’s breakdown. Note that not all Markdown features are supported within Notion. The table below gives you a quick reference to all of the Markdown commands available to you within Notion. From there, choose Markdown & CSV as your export format. You can find this at the top-right 3-dot menu → Export. md file, which will contain all the original markup. While Notion does this, it also gives you to option to export a plaintext. Some Markdown purists don’t like WYSIWYG Markdown editors because they strip away the markup characters. You’ll also find this type of Markdown system in apps like Slite, Roam Research, Bear, and Whimsical Docs (which might have my favorite text editor of any app – ask me why on Twitter). Java is a registered trademark of Oracle and/or its affiliates.Notion applies Markdown formatting immediately. For details, see the Google Developers Site Policies. Next, take a look at the tutorial for training a DQN agent on the Cartpole environment using TF-Agents.Įxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Additionally, TF-Agents supports TensorFlow 2.0 mode, which enables us to use TF in imperative mode. These components are implemented as Python functions or TensorFlow graph ops, and we also have wrappers for converting between them. TF-Agents provides all the components necessary to train a DQN agent, such as the agent itself, the environment, policies, networks, replay buffers, data collection loops, and metrics. This has two advantages: better data efficiency by reusing each transition in many updates, and better stability using uncorrelated transitions in a batch. Then during training, instead of using just the latest transition to compute the loss and its gradient, we compute them using a mini-batch of transitions sampled from the replay buffer. At each time step of data collection, the transitions are added to a circular buffer called the replay buffer. The Atari DQN work introduced a technique called Experience Replay to make the network updates more stable. At each time step, the agent takes an action on the environment based on its policy \(\pi(a_t|s_t)\), where \(s_t\) is the current observation from the environment, and receives a reward \(r_\), this reduces to standard Q-Learning. The agent and environment continuously interact with each other. The two main components are the environment, which represents the problem to be solved, and the agent, which represents the learning algorithm. Reinforcement learning (RL) is a general framework where agents learn to perform actions in an environment so as to maximize a reward.
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