OpenAI Gym Introduction and Improvement (i) GYM environment construction and the simplest RL agent

OpenAI Gym is a toolkit for developing and comparing RL algorithms that is compatible with other numerical computing libraries, such as TensorFlow or Theano libraries. The Python language is now primarily supported and will be supported in other languages later. The gym document is in Https://gym.openai.com/docs.

OpenAI Gym consists of 2 parts:

1, gym Open Source Library: Contains a test problem set, each problem becomes the environment (environment), can be used for their own RL algorithm development. These environments have shared interfaces that allow users to design common algorithms. It contains the Atari game test bed used by deep mind.

2. OpenAI Gym Service: Provides a site and API to allow users to compare the algorithms they have trained for performance.

In short, the OpenAI gym is an RL algorithm for the test bed (testbed).

There are 2 basic concepts in reinforcement learning, one is the environment (environment), called the outside world, and the other is the agent for agents (written algorithm). The agent sends action to Environment,environment to return observations and returns.

Gym's core interface is Env, which serves as a unified environment interface. Env contains the following core methods:

1. Reset (self): Resets the state of the environment and returns to observation.

2, Step (self,action): Advance A time step, return Observation,reward,done,info

3. Render (self,mode= ' human ', Close=false): Redraws A frame of the environment. The default mode is generally friendly, such as a popup window.

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For more information, please download the PDF document below:

1-20160618-openaigym Introduction and improvement of -1GYM environment construction and the simplest rlagent.pdf

OpenAI Gym Introduction and Improvement (i) GYM environment construction and the simplest RL agent