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April 5, 2017

they're used to log you in. It’s especially frustrating when the work is concurrent, which happens from time to time! But even if a project like this fails, it often leads the researcher to many new insights that become fertile soil for the next project. This is a common failure mode for people who are new to deep RL, and if you find yourself stuck in it, don’t be discouraged—but do try to change tack and work on a simpler algorithm instead, before returning to the more complex thing later. Don’t just take the results from the best or most interesting runs to use in your paper. Frame 3: Create a New Problem Setting. For projects along these lines, a standard benchmark probably doesn’t exist yet, and you will have to design one. Deep RL refers to the combination of RL with deep learning. We're going to host a workshop on Spinning Up in Deep RL at OpenAI San Francisco on February 2nd 2019. Writing working RL code requires clear, detail-oriented understanding of the algorithms. To get there, you’ll need an idea for a project. Instead of thinking about how to improve an existing method, you aim to succeed on a task that no one has solved before. If you’ve invented a new test domain, so there’s no previous SOTA, you still need to try out whatever the most reliable algorithm in the literature is that could plausibly do well in the new test domain, and then you have to beat that. Ideal attendees have software engineering experience and have tinkered with ML but no formal ML experience is required. These aren’t strictly necessary, and some of the best-reported results for DDPG use simpler networks. If nothing happens, download the GitHub extension for Visual Studio and try again. These habits are worth keeping beyond the stage where you’re just learning about deep RL—they will accelerate your research! Using OpenAI's Spinning Up. These small-scale experiments don’t require any special hardware, and can be run without too much trouble on CPUs. Measure everything. Problems in this frame come up when they come up—it’s hard to go looking for them. Developing that knowledge requires you to engage with both academic literature and other existing implementations (when possible), so a good amount of your time should be spent on that reading. Do good research and finish out your projects with complete and thorough investigations, because that’s what counts, and by far what matters most in the long run. There are a wide range of topics you might find interesting: sample efficiency, exploration, transfer learning, hierarchy, memory, model-based RL, meta learning, and multi-agent, to name a few. It is possible for a novice to approch this kind of problem, but there will be a steeper learning curve. Also, do your best to hold “all else equal” even if there are substantial differences between your algorithm and the baseline. Once you feel reasonably comfortable with the basics in deep RL, you should start pushing on the boundaries and doing research. Know what states, actions, trajectories, policies, rewards, value functions, and action-value functions are. But don’t let that deter you—and definitely don’t let it motivate you to plant flags with not-quite-finished research and over-claim the merits of the partial work. We've also seen that being competent in RL can help people participate in interdisciplinary research areas like AI safety, which involve a mix of reinforcement learning and other skills. Limiting its usage to Linux and Mac means shutting the door to many potential users, contributors and people that eager to learn. Know about standard architectures (MLP, vanilla RNN, LSTM (also see this blog), GRU, conv layers, resnets, attention mechanisms), common regularizers (weight decay, dropout), normalization (batch norm, layer norm, weight norm), and optimizers (SGD, momentum SGD, Adam, others). Work fast with our official CLI. It turns out that the baselines in RL are pretty strong, and getting big, consistent wins over them can be tricky or require some good insight in algorithm design. If you’re looking for inspiration, or just want to get a rough sense of what’s out there, check out Spinning Up’s key papers list. After you have an implementation of an RL algorithm that seems to work correctly in the simplest environments, test it out on harder environments. It’s also the case that there are a lot of great resources out there, but the material is new enough that there’s not a clear, well-charted path to mastery. If you start off trying to build something with too many moving parts, odds are good that it will break and you’ll lose weeks trying to debug it. Use Git or checkout with SVN using the web URL. For our first partnership, we’re working with the Center for Human-Compatible AI (CHAI) at the University of California at Berkeley to run a workshop on deep RL in early 2019, similar to the planned Spinning Up workshop at OpenAI. Status: Maintenance (expect bug fixes and minor updates). But while you’re still in the early stages with it, do the most thorough check you can to make sure it hasn’t already been done. Focus on understanding. For more information, see our Privacy Statement. Beware of random seeds making things look stronger or weaker than they really are, so run everything for many random seeds (at least 3, but if you want to be thorough, do 10 or more). When you come up with a good idea that you want to start testing, that’s great! The workshop will consist of 3 hours of lecture material and 5 hours of semi-structured hacking, project-development, and breakout sessions - all supported by members of the technical staff at OpenAI. You should implement as many of the core deep RL algorithms from scratch as you can, with the aim of writing the shortest correct implementation of each. This is to enforce a weak form of preregistration: you use the tuning stage to come up with your hypotheses, and you use the final runs to come up with your conclusions. Spinning Up should also work on Windows though. We have the following support plan for this project: 1. If nothing happens, download GitHub Desktop and try again. For example, the original DDPG paper suggests a complex neural network architecture and initialization scheme, as well as batch normalization. Thanks to the many people who contributed to this launch: Alex Ray, Amanda Askell, Ashley Pilipiszyn, Ben Garfinkel, Catherine Olsson, Christy Dennison, Coline Devin, Daniel Zeigler, Dylan Hadfield-Menell, Eric Sigler, Ge Yang, Greg Khan, Ian Atha, Jack Clark, Jonas Rothfuss, Larissa Schiavo, Leandro Castelao, Lilian Weng, Maddie Hall, Matthias Plappert, Miles Brundage, Peter Zokhov & Pieter Abbeel. Even if we all agree that Windows is a lesser system, we can't deny that is widely spread one, one that teaching material such as Spinning Up shouldn't ignore. We've had so many people ask for guidance in learning RL from scratch, that we've decided to formalize the informal advice we've been giving. Learn more. Learn more. Write single-threaded code before you try writing parallelized versions of these algorithms. Become familiar with at least one deep learning library. We favor clarity over modularity—code reuse between implementations is strictly limited to logging and parallelization utilities. ML Engineering for AI Safety & Robustness: a Google Brain Engineer’s Guide to Entering the Field, by Catherine Olsson and 80,000 Hours. Your ideal experiment turnaround-time at the debug stage is <5 minutes (on your local machine) or slightly longer but not much. Build up a solid mathematical background. Reimplementing prior work is super helpful here, because it exposes you to the ways that existing algorithms are brittle and could be improved. Specialized hardware—like a beefy GPU or a 32-core machine—might be useful at this point, and you should consider looking into cloud computing resources like AWS or GCE. Participants came from a wide range of backgrounds, including academia, software engineering, data science, ML engineering, medicine, and education. Because projects like these are tied to existing methods, they are by nature narrowly scoped and can wrap up quickly (a few months), which may be desirable (especially when starting out as a researcher). The best way to get a feel for how deep RL algorithms perform is to just run them. You don’t need to know how to do everything, but you should feel pretty confident in implementing a simple program to do supervised learning. This lets you make each separate claim with a measure of confidence, and increases the overall strength of your work. If you implement your baseline from scratch—as opposed to comparing against another paper’s numbers directly—it’s important to spend as much time tuning your baseline as you spend tuning your own algorithm. If you reference or use Spinning Up in your research, please cite: @article{SpinningUp2018, author = {Achiam, Joshua}, title = {{Spinning Up in Deep Reinforcement Learning}}, year = {2018} } Any method you propose is likely to have several key design decisions—like architecture choices or regularization techniques, for instance—each of which could separately impact performance. Approaches to idea-generation: There are a many different ways to start thinking about ideas for projects, and the frame you choose influences how the project might evolve and what risks it will face. Revision 038665d6. You can always update your selection by clicking Cookie Preferences at the bottom of the page. For the unfamiliar: reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. In order to validate that your proposal is a meaningful contribution, you have to rigorously prove that it actually gets a performance benefit over the strongest possible baseline algorithm—whatever currently achieves SOTA (state of the art) on your test domains.

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