Measuring the Success of Reinforcement Learning: Epsilon Convergence Rate (ECR-ε)
Reinforcement learning (RL) is a subfield of machine learning that enables agents to learn and improve their performance through trial and error interactions with the environment. To gauge the success of an RL agent, we need a reliable metric that captures its improvement over time. This is where the Epsilon Convergence Rate (ECR-ε) comes into play.
What is ECR-ε?
ECR-ε measures the rate at which the agent's performance improves. Specifically, it quantifies the convergence rate of the average reward or cumulative reward to a target value. In other words, ECR-ε estimates the speed at which the agent learns to achieve its goals.
Formulation of ECR-ε
Mathematically, ECR-ε can be formulated as follows:
ECR-ε (N) = 1 - (1 - ε)^N
where ε is the learning rate, N is the number of episodes, and ECR-ε is the convergence rate.
Interpretation of ECR-ε
A higher ECR-ε value indicates faster ...
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