Myself Amartya Singh and I have completed my graduation from Delhi University in 2018. I love to be a common knowledgeable person hence I have to join this company as a content writing in different areas on multiple topics.
Definition: Reinforcement learning is a branch of machine learning where an agent learns to make decisions by interacting with an environment to achieve a goal.
Unlike supervised learning (where labeled data provides answers), RL relies on trial and error.
Key Concepts:
Agent: The learner that interacts with the environment.
Environment: The external system with which the agent interacts.
Reward: Feedback received by the agent for its actions (positive or negative).
Goal: The agent aims to maximize cumulative rewards over time.
How It Works:
The agent takes actions in the environment.
Based on these actions, it receives rewards or penalties.
Over time, the agent learns optimal strategies to maximize rewards.
Example:
Imagine a robot navigating obstacles to reach a diamond (reward) while avoiding fire (penalty).
The robot explores different paths, learning which actions lead to rewards.
Correct steps increase the robot’s reward, while wrong steps decrease it.
The total reward accumulates until the robot reaches the diamond.
RL Algorithms:
Q-Learning: An iterative algorithm that converges to optimal values.
Deep Q Networks (DQN): Combines Q-learning with deep learning.
Policy Gradient: A policy-based deep-learning approach.
Actor-Critic: A sophisticated hybrid of DQN and policy gradient.
Liked By
Write Answer
How does reinforcement learning work?
Join MindStick Community
You have need login or register for voting of answers or question.
Bhavesh Badani
16-May-2024What is Reinforcement Learning?
Key Concepts:
How It Works:
Example:
RL Algorithms: