Thank you! Check out my Tutorials to learn more! Your software has transformed the way I create every timelapse now! I wanted to let you know that LRT was behind all the timelapse.
I created a showcase of all the timelapse shots you can see here. I love it. Without it everything took for ages, now i have the total control over my footage. I am succeeding it producing day to night TL with very little flicker.
Everything is working awesome! It is packed with many useful features that have helped take my work to the next level. The software is also fairly intuitive, and easy to learn especially after watching the tutorial videos found on LRTimelapse. Bryan Snider — Check out his full review and personal story. Even though I have never done a time-lapse before, I completely understood your tutorials and program and why it works.
A run through with the sub frame lapse with no keyframe wizard even produced a pleasing result. Thanks a lot man and I am stoked to try out the license and start adding these awesome lapse features to my video.
Most importantly I wanted to say great job on everything about this program; templates, book and all. Rock On! I just released a blog post about my journey to become successful in the creation of HG timelapses. German Article by langzeit-zeitraffer. Have been using it occasionally for some time now but just recently have been using it all day every day. Fantastic work, much appreciated by myself and the time-lapse community as a whole.
Still, the development process seems to be a slow-going one. To sum up, Pyqlearning leaves much to be desired. It is not a library that you will use commonly. Thus, you should probably use something else.
Tensorforce has key design choices that differentiate it from other RL libraries:. Besides that it is perfect. It is quite easy to start using Tensorforce thanks to the variety of simple examples and tutorials.
The official documentation seems complete and convenient to navigate through. Tensorforce benefits from its modular design. Each part of the architecture, for example, networks, models, runners is distinct. Thus, you can easily modify them.
However, the code lacks comments and that could be a problem. It also has documentation to help you plug into other environments. To sum up, Tensorforce is a powerful RL tool. It is up-to-date and has all necessary documentation for you to start working with it.
The components of the library, for example, algorithms, environments, neural network architectures are modular. Thus, extending and reusing existent components is fairly painless. Still, you should check the official installation tutorial as a few prerequisites are required. The documentation is complete. It will be easy for newcomers to start working with it.
It benefits from the modular design, but the code lacks comments. For more information including installation and usage instructions please refer to official documentation. Coach supports various logging and tracking tools. It even has its own visualization dashboard.
For usage instructions please refer to the documentation. I would strongly recommend Coach. TFAgents is a Python library designed to make implementing, deploying, and testing RL algorithms easier. It has a modular structure and provides well-tested components that can be easily modified and extended. TFAgents is currently under active development, but even the current set of components makes it the most promising RL library. TFAgents has a series of tutorials on each major component.
Still, the official documentation seems incomplete, I would even say there is none. However, the tutorials and simple examples do their job, but the lack of well-written documentation is a major disadvantage. The code is full of comments and the implementations are very clean. TFAgents seems to have the best library code.
As mentioned above, TFAgents is currently under active development. The last update was made just a couple of days ago. To sum up, TFAgents is a very promising library. It already has all necessary tools to start working with it. I wonder what it will look like when the development is over.
The OpenAI Baselines library was not good. Stable Baselines features unified structure for all algorithms, a visualization tool and excellent documentation. The documentation is complete and excellent. The set of tutorials and examples is also really helpful.
On the other hand, modifying the code can be tricky. But because Stable Baselines provides a lot of useful comments in the code and awesome documentation, the modification process will be less complex. Stable Baselines provides good documentation about how to plug into your custom environment, however, you need to do it using OpenAI Gym. Vectorized environment feature is supported by a majority of the algorithms.
Please check the documentation in case you want to learn more. The last major updates were made almost two years ago, but the library is maintained as the documentation is regularly updated. To sum up, Stable Baselines is a library with a great set of algorithms and awesome documentation.
You should consider using it as your RL tool. MushroomRL is a Python Reinforcement Learning library whose modularity allows you to use well-known Python libraries for tensor computation and RL benchmarks. The idea behind MushroomRL consists of offering the majority of RL algorithms, providing a common interface in order to run them without doing too much work.
The official documentation seems incomplete. It misses valuable tutorials, and simple examples leave much to be desired.
The code lacks comments and parameter description. Although MushroomRL never positioned itself as a library that is easy to customize. MushroomRL supports various logging and tracking tools. I would recommend using TensorBoard as the most popular one. To sum up, MushroomRL has a good set of algorithms implemented. Still, it misses tutorials and examples which are crucial when you start to work with a new library.
From my experience, RLlib is a very powerful framework that covers many applications and at the same time remains quite easy to use. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas speculative research. If you look for a customizable framework with well-tested DQN based algorithms, then this may be your pick.
They are almost completely self-contained, with virtually no common code shared between them except for logging, saving, loading, and MPI utilities , so that an interested person can study each algorithm separately without having to dig through an endless chain of dependencies to see how something is done.
The implementations are patterned so that they come as close to pseudocode as possible, to minimize the gap between theory and code. Although it was created as an educational resource, the code simplicity and state-of-the-art results make it a perfect framework for fast prototyping your research ideas.
I use it in my own research and even implement new algorithms in it using the same code structure. If such a tool is something you need, i. Acme strives to expose simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research.
The design of Acme also attempts to provide multiple points of entry to the RL problem at differing levels of complexity. Acme is simple like SpinningUp but a tier higher if it comes to the use of abstraction. It makes it easier to maintain — code is more reusable — but on the other hand, harder to find the exact spot in the implementation you should change when tinkering with the algorithm.
This makes coax more modular and user-friendly for RL researchers and practitioners. I would recommend coax for education purposes. If you want to plug-n-play with nitty-gritty details of RL algorithms, this is a good tool to do this. We introduce Surreal, an open-source, reproducible, and scalable distributed reinforcement learning framework.
Surreal provides a high-level abstraction for building distributed reinforcement learning algorithms. I include this framework on the list mostly for reference.
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