Py-gym

OpenAI toolkit for developing and comparing your reinforcement learning agents

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the gym open-source library, which gives you access to a standardized set of environments.

gym makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages.

There are two basic concepts in reinforcement learning the environment namely, the outside world and the agent namely, the algorithm you are writing. The agent sends actions to the environment, and the environment replies with observations and rewards that is, a score.

The core gym interface is Env, which is the unified environment interface. There is no interface for agents; that part is left to you. The following are the Env methods you should know

• resetself Reset the environment’s state. Returns observation.
• stepself, action Step the environment by one timestep. Returns observation, reward, done, info.
• renderself, mode=’human’ Render one frame of the environment. The default mode will do something human friendly, such as pop up a window.