OpenAI Gym
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.
Note
Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports the ability to teach agents everything from walking to playing games such as Pong or Pinball. The library is available at the following URL:
