This is the Windows app named D4RL whose latest release can be downloaded as D4RLsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named D4RL with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
D4RL
DESCRIPTION
D4RL (Datasets for Deep Data-Driven Reinforcement Learning) is a benchmark suite focused on offline reinforcement learning — i.e., learning policies from fixed datasets rather than via online interaction with the environment. It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. Researchers can load a dataset for a given task (e.g., maze navigation, manipulation) and apply their algorithm without the need to collect fresh transitions, which accelerates experimentation and comparison. The API is based on Gymnasium (via gym.make) and each environment also exposes a method get_dataset() that returns the offline data to learn from. The repository emphasizes open science, reproducibility, and benchmarking at scale, making it easier to compare algorithms on equal footing.
Features
- Offline reinforcement-learning benchmark suite with fixed datasets and tasks
- gym.make compatible environments plus _get_dataset() method returning transitions
- Support for algorithm comparison, reproducibility and standardized tasks
- Large variety of tasks (navigation, manipulation, maze, robotics) and datasets
- Open licensing (Apache-2.0 for code, CC BY for data) to facilitate research and attribution
- Used widely in RL research for benchmarking offline learning algorithms
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/d4rl.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
