What is exploration vs exploitation?

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In Reinforcement Learning (RL), exploration and exploitation are two fundamental strategies an agent uses to make decisions while learning in an environment. Balancing them is key to achieving optimal long-term rewards.

🔹 1. Exploration

• Definition: The agent tries new or less-known actions to discover their effects and potential rewards.

• Purpose: To gather more information about the environment and avoid missing better strategies.

• Example: A robot in a maze tries a new path it hasn’t taken before to see if it leads to the goal faster.

• Pros: Helps discover optimal actions.

• Cons: May temporarily reduce immediate rewards.

🔹 2. Exploitation

• Definition: The agent chooses actions that it already knows yield high rewards, based on past experience.

• Purpose: To maximize immediate reward using existing knowledge.

• Example: A robot repeatedly chooses a known path in the maze because it already leads to the goal reliably.

• Pros: Maximizes short-term rewards.

• Cons: May miss even better actions or paths.

🔹 Balancing Exploration and Exploitation

• Known as the exploration-exploitation trade-off.

• Common strategies:

  • ε-greedy: With probability ε, explore; otherwise, exploit.

  • Softmax action selection: Probabilistically choose actions based on expected reward.

  • Upper Confidence Bound (UCB): Balances reward and uncertainty to select actions.

✅ In short:

• Exploration: Try new actions to gain knowledge.

• Exploitation: Use known actions to maximize reward.

• Trade-off: Too much exploration slows reward accumulation; too much exploitation may miss better solutions.

Read more:

What is deep Q-learning?

What is policy gradient?

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