Author(s): Cornellius Yudha Wijaya

Originally published on Towards AI.

Learn the reinforcement learning hands-on with these packages

Photo by Tinky Delta on Unsplash

Reinforcement Learning is a machine learning study field with concerns about optimal decision-making. By learning from the environment, Reinforcement Learning would find the parameter that maximizes rewards.

Unlike supervised or unsupervised learning, Reinforcement Learning uses an independent learner (agent) to learn the environment without any labels or directions. The learner must follow specific policies and reward systems set before learning.

Essentially, Reinforcement Learning requires four elements:

LearnerEnvironmentPolicy ActionReward

In general, there would be a valuable function in which the algorithm would try to maximize the reward average from learning the environment based on the action.

It might not sound very… Read the full blog for free on Medium.

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Published via Towards AI