Last Updated on July 17, 2023 by Editorial Team
Author(s): Renu Khandelwal
Originally published on Towards AI.
A Simple Explanation of Policy Gradient for Reinforcement Learning with very little Math
Your goal is to teach a simulated robot to move forward using Reinforcement Learning(RL).
A simulated robot must explore the unknown environment with an infinitely large number of actions it can take to find which actions yield the best results to keep moving forward.
The goal of the simulated robot is to find optimal actions for a given state in an environment to maximize the long-term reward of moving forward. The goal can be achieved using an optimized policy that maps the robot's optimal actions for the different environmental states.
The challenge for the robot is to choose actions from an infinitely large… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI