OpenAI GYM is a toolkit developers use to both develop and compare reinforcement learning algorithms. Their GitHub repository includes dozens of contributors. They offer a leaderboard so contributors can see how their enhancements to reinforcement learning algorithms compare to others. There are essentially two parts to OpenAI gym: the open source library and the service that includes their API. Even if you’re not incredibly technologically savvy, a contributor can participate by reproducing experiment results. That presents a good place to start for those who are unsure of creative ways to alter the learning algorithms to experiment with different results. OpenAI Gym contains a variety of environments and examples for testing reinforcement algorithms. The CartPole example balances a pole on a cart for a short amount of time. The MountainCar example has a car drive up a big hill. Other fun examples include Atari and board games and even Minecraft examples.
- The documents for OpenAI Gym provide code to get a bare minimum CartPole example running.
- This makes it easy for a newcomer to the project to get up and running in the shortest time possible.
- OpenAI Gym also has an API that allows contributors to compare the performance of their trained AI agents.
- Engaged community of developers who can provide feedback or technical help if needed
- They have a notable list of individual sponsors and companies that support their mission, including individuals like Sam Altman, Elon Musk, and Peter Thiel and companies like Microsoft and Infosys.
- If you’re interested in developing proprietary AI technology, visibly including it on an open source project would be counterproductive to your goals.
- Their mission might not even be attainable. AI is such a burgeoning field that it might not be possible to develop “safe AI.” There might be some inherent component of AI that allows to overtake humans without any ability to thwart that.
OpenAI Gym is really the best of its’ kind. Most AI development including reinforcement learning is done at a proprietary level. The transparency and open source nature of this software increases the chances that of achieving safe artificial intelligence that is fairly available to all. If “friendly artificial intelligence is even possible, this project will achieve it.