Artificial intelligence is here for everyone to use one way or the other.
As for OpenAI Gym, there are many explorable training grounds to feed your reinforcement learning agents.
What is OpenAI Gym, how does it work, and what can you build using it?
What Is OpenAI Gym?
While the agent aims to maximize rewards, it gets penalized for each unexpected decision.
The time step is a discrete-time tick for the environment to transit into another state.
It adds up as the agent’s actions change the environment state.
How Does OpenAI Gym Work?
Thus, it follows that rewards only come when the environment changes state.
The racetrack changes at each time step and might get more complicated in subsequent states.
Negative rewards or penalties aren’t bad for an agent in reinforcement learning.
In some cases, it encourages it to achieve its goal more quickly.
Thus, the car learns about the track over time and masters its navigation using reward streaks.
While it may successfully avoid more holes the next time, it may get no reward.
But modifying a few parameters might improve its learning speed.
How to Get Started With OpenAI Gym
OpenAI Gym supports Python 3.7 and later versions.
it’s possible for you to create a custom environment, though.
But start by playing around with an existing one to master the OpenAI Gym concept.
The code below spins up theFrozenLake-v1.
Theenv.resetmethod records the initial observation:
Some environments require extra libraries to work.
If you should probably install another library, Python recommends it via the exception message.
For example, you’ll install an additional library (gymnasium[toy-text]) to run theFrozenLake-v1environment.
While some of these tools are low-cost, others, including the OpenAI Gym, are free and open-source.
