OpenAI Gym is a library that helps us to implement algorithms based on reinforcement learning. It includes a growing collection of benchmark issues that expose a common interface, and a website where people can share their results and compare algorithm performance.
OpenAI Gym focuses on the episodic setting of reinforced learning. In other words, the agent's experience is divided into a series of episodes. The initial state of the agent is randomly sampled by a distribution, and the interaction proceeds until the environment reaches a terminal state. This procedure is repeated for each episode, with the aim of maximizing the total reward expectation per episode and achieving a high level of performance in the fewest possible episodes.
Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports the ability to teach agents everything...
