Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
Tradeoffs between Explore-Exploit
3.
Epsilon Greedy Policy
4.
Difference Between Exploration and Exploitation
5.
Regret in Exploration
6.
Greedy Algorithm in Exploration and Exploitation
7.
FAQs
8.
Key Takeaways
Last Updated: Mar 27, 2024

TradeOffs like Exploration vs. Exploitation

Author Tashmit
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Besides supervised and unsupervised learning, reinforcement learning is also a machine learning paradigm. Its goal is to train the machine to understand and learn to take action that will maximize the reward. The strategy of trying and testing does it. The agent is responsible for accomplishing this by finding the optimal balance between exploring new environments and exploiting the already learned environments. 

Tradeoffs between Explore-Exploit

  • Exploitation is when the agent knows all his options and chooses the best option based on the previous success rates.
  • Exploration is the concept where the agent is unaware of his opportunities and tries to explore other options to predict better and earn rewards.

 

Dilemma and decision are the two different sides of the same situation. Let us take the example of two friends investing/buying shares. One friend gets lucky and increases his investment by three times. While the other did not get any profit, at this point, he gets greedy and thinks that he might also get lucky if he was investing in the same company as his friend.

Source: Link

So he then starts investing in the same company as his friend. This action is called the greedy action, and the policy is the greedy policy.  

However, the share market is quite unpredictable; he might not know whether the company share prices would hike or decline or stay the same, and therefore the greedy policy would fail. Similarly, in reinforcement learning, having a partial understanding of future states and rewards, the agent will be in a dilemma whether to explore unknown actions or exploit the limited knowledge to receive rewards. The agent cannot go for both exploit and explore simultaneously. Therefore, we imply the Epsilon Greedy Policy to overcome exploration and exploitation's tradeoffs.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Epsilon Greedy Policy

We use the Epsilon Greedy Algorithm to explore the tradeoffs between exploration and exploitation. The algorithm works by randomly choosing between exploitation or exploration.

Suppose we are tossing a coin if it is heads we will explore; if its tails, we will exploit. 

Source: Link

Here the probability of exploration and exploitation is 1/2, as is the probability of heads or tails. This technique is known as Epsilon Greedy Action, where Epsilon is known as the probability to choose exploration and helps in the tradeoffs between exploration and exploitation. 

The multi-armed bandit problem is a classic example of tradeoffs between exploration and exploitation. To get an in-depth understanding of the Epsilon Greedy Algorithm and multi-armed bandit problem, visit this article.  

Difference Between Exploration and Exploitation

Exploration

Exploitation

The agent is unaware of his opportunities and tries to explore other options to predict better and earn rewards.

The agent is well aware of its options and chooses the best options based on the previous success rate.

Search space in much diverse

Search space is consistent

It involves search, variation, risk-taking, experimentation, discovery and, innovation.

It involves refinement, efficiency, selection, implementation and, execution.

Check this out, Difference Between Analog and Digital Computer

Regret in Exploration

Instead of calculating the reward, we might think about how much loss we are having and make the decision better. This is known as regret. 

It is denoted by V*, which can be calculated by

Source: Link

Greedy Algorithm in Exploration and Exploitation

We know that the greedy algorithm decides exploration and exploitation. A greedy action is an action whose estimated value is most significant. If there are more greedy actions, a selection is arbitrarily made among them, perhaps randomly. The greedy algorithm also takes decisions based on regret.

Source: Link

The greedy algorithm is responsible for selecting between exploration and exploitation. In contrast, the Epsilon greedy is responsible for choosing the action with the highest rewards. On the other hand, the decayed epsilon greedy algorithm is the improved version of the greedy epsilon algorithm.

FAQs

  1. Why is exploration critical?
    It is always better to learn and explore more. Therefore, learning new and diverse outcomes can help the model work better. 
     
  2. What are the steps involved in exploration?
    It includes actions like search, variation, risk-taking, experimentation, discovery, and innovation. 
     
  3. What are the steps involved in exploitation?
    It includes refinement, efficiency, selection, implementation, and execution.

Key Takeaways

Trying and testing to maximize the rewards is how Reinforcement Learning works. The tradeoff between exploration and exploitation has always been a dilemma overcome by Epsilon greedy Policy. To get an in-depth understanding of Reinforcement Learning, check out this article

Previous article
What is Genetic Algorithm?
Next article
Introduction to the Actor-Critic Model
Live masterclass