What is a Epsilon Greedy Algorithm?

Описание: The Epsilon-Greedy Algorithm is a simple strategy used in reinforcement learning and optimization problems that involve exploration and exploitation. It's a way to balance between exploring unknown options and exploiting the current best-known option.


https://www.geeksforgeeks.org/epsilon...

Here's how the Epsilon-Greedy Algorithm works:

Exploration vs. Exploitation:

Exploitation: Choose the action that is believed to be the best based on current knowledge.
Exploration: Occasionally choose a random action to explore and discover potentially better actions.
Epsilon Parameter:

The algorithm introduces a parameter called epsilon (ε), which is a small positive value between 0 and 1.
Decision Making:

With probability 1 - ε (1 minus epsilon), the algorithm exploits the current best-known action.
With probability ε (epsilon), the algorithm explores and chooses a random action regardless of its current knowledge.
Implementation:

At each decision point, a random number is generated. If this number is less than or equal to ε, then a random action is chosen. Otherwise, the action with the highest estimated value (exploitation) is selected.
The idea behind Epsilon-Greedy is to ensure that the agent explores various actions, even if they don't seem optimal based on current knowledge. This helps in discovering potentially better actions that might lead to higher rewards. Over time, as the agent gathers more information, the epsilon value might be reduced to favor exploitation of the learned knowledge.

The balance between exploration and exploitation is crucial in reinforcement learning, especially when dealing with uncertain environments or when the agent's knowledge is incomplete. The Epsilon-Greedy Algorithm provides a simple and effective way to manage this trade-off.

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