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Trade-off Between Exploration and Exploitation, and Epsilon(ε-) Greedy Exploration - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In reinforcement learning, the trade-off between trying out new actions to discover their effects (exploration) and choosing actions that are known to yield good rewards based on past experience (exploitation) is a crucial aspect of learning. Striking the right balance between exploration and exploitation is a key challenge in reinforcement learning. Common strategies to balance exploration and exploitation include:
Epsilon-greedy exploration is a strategy used in reinforcement learning and multi-armed bandit problems to balance exploration and exploitation. The basic idea is to choose the action that currently seems to be the best most of the time (exploitation), but with a small probability, epsilon (ε), choose a random action instead (exploration). The value of ε is a hyperparameter that determines the balance between exploration and exploitation. If ε is set to a high value, the algorithm will explore more, potentially discovering better actions, but it might sacrifice short-term gains by not exploiting the current best action. If ε is set to a low value, the algorithm will exploit more, focusing on the currently best-known action, but it might miss out on discovering better actions. The range of values for ε typically falls between 0 and 1. Specifically, ε is a probability, so its values are constrained to the interval [0, 1]. The epsilon-greedy strategy uses this parameter to determine the probability of exploration versus exploitation.
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