What is exploration-exploitation trade-off in RL?

Exploration-exploitation trade-off — this is a fundamental dilemma in reinforcement learning (RL), related to choosing between:

• Exploration — trying new actions to learn more about the environment and potentially find more profitable strategies.
• Exploitation — using already known actions that yield the best expected result.

If an agent explores too much, it may waste resources on ineffective actions and not utilize accumulated knowledge. Conversely, if it switches to exploitation too early, it may get stuck in a locally optimal solution and miss better options.

The balance between exploration and exploitation is achieved through various strategies, such as:

• ε-greedy policy: with probability ε, the agent chooses a random action (exploration), and with probability 1-ε — the best known (exploitation).
• Probabilistic selection methods, such as Softmax.

Proper management of this trade-off is critical for effective agent training and achieving optimal behavior policy.