Reinforcement Learning: Creating a Custom Environment
Create a New Custom Environment using Open AI Gym
In this article, you will learn how to create a custom Environment using OpenAI Gym to train the Agent.
Reinforcement learning maps observations to actions to maximize the long-term reward. The Agent explores the Environment and finds the actions for which the Environment yields the most rewards.
Before you create a custom Environment, it is essential to know the characteristics of the RL Environment and how they are mapped in the OpenAI Gym
OpenAI Gym is a toolkit for Reinforcement learning research, and you will use it to create a custom Environment
The gym is an open-source Python library for developing and comparing reinforcement learning algorithms. The Gym API models Reinforcement Learning environments as simple Python env classes, making Environment creation and interacting with it very simple.
Reinforcement Learning Environment has the following characteristics
- State or Observation: A state or observation is a moment or instance of the…
