In this part of the series, we explore the Greedy Exploration strategy, also known as the Epsilon-Greedy Strategy, in Reinforcement Learning using the Multi-Armed Bandit problem. I walk you through the implementation of this powerful approach, where the agent balances between exploring new options and exploiting the most rewarding ones.
Through a Python-based implementation, I explain how the greedy exploration works by selecting arms with the highest observed rewards while still allowing random exploration when necessary. The use of an epsilon parameter adds flexibility to control the balance between exploration and exploitation, ensuring that no arm is left unchecked.
This episode is perfect for those looking to understand more efficient RL strategies and how the Epsilon-Greedy Algorithm can optimize decision-making in dynamic environments. Tune in for a deep dive into the mechanics behind this popular exploration technique.
Негізгі бет Reinforcement Learning - Multi Armed Bandit | Epsilon-Greedy Strategy for Multi-Armed Bandit Ch. 4
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