TradeOffs like Exploration vs. Exploitation

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. 

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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

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

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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.

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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.