By: Alexander Mervar
OpenAI gym is a highly accessible and maximally convenient way of combining deep and reinforced learning for AI agents. It’s collection of environments, diversify the challenges and AI can face. The Gym has a common interface to make it very easy to access different environments. It also has the capability of expanding to more environments. Due to this, the OpenAI Gym has also made it a priority to maintain the capability to reproduce meaningful results as time progresses and the development of the Gym continues. This focus on the environments of the gym shows that the creators of the OpenAI Gym are aware of the different styles of AI development and processing, which are “online learning” and “batch update” learning. To maintain focus on AI quality, the Gym puts emphasis on the time it takes an agents to complete its task. The environment does not solely focus on the end product. This is due to the fact that when examining agents, the end product can be improved by computational resources and not AI quality. Thus, a focus on sample complexity is necessary to evaluate the agents.
The authors and creators of OpenAI Gym are aware of the many challenges that face many areas of AI development. Thus, there is an emphasis on the real world applications of the technology at hand and the OpenAI Gym reflets these areas where necessary attention is warranted. The authors share the reasoning and thought process behind every one of their steps. This is supplemented by the exampling of code as well as screenshots of several environments that are available in the OpenAI Gym.
The publication of not only this paper but the production of the OpenAI Gym allows for ease-of-access AI development tools, which are incredibly sparse in the world of AI development. This capability radically changes the way that AI can produced and exponentially increased the chances for a powerful agent to be created. Like AI Safety Gridworlds, these environments also allow one to be able to recognize fallacies in the agent and make necessary adjustments to optimize the agent’s performance.