How does reinforcement learning work?

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Bhavesh Badani · Answer

What is Reinforcement Learning?
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.

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How does reinforcement learning work?

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