This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Measuring Success in Reinforcement Learning: Unpacking the Exploration-Exploitation Efficiency Score (EEES)
When it comes to evaluating the performance of reinforcement learning (RL) agents, one crucial metric stands out: the Exploration-Exploitation Efficiency Score (EEES). This score provides a comprehensive assessment of an agent’s ability to balance exploration and exploitation, two fundamental aspects of RL.
What is EEES?
The EEES is calculated as (Exploration Rate x Average Reward) / (Standard Deviation of Rewards). This formula captures three essential components:
- Exploration Rate: A measure of how often the agent explores its environment, rather than exploiting known optimal actions.
- Average Reward: The average reward received by the agent over a certain period.
- Standard Deviation of Rewards: A measure of the variability in rewards, indicating how stable the agent’s performance is.
Interpreting EEES
A score above 0.5 indicates a well-ba…
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This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez