Optimizing Electric Vehicle Charging Management with Hyperparameter Tuning

In this article, we explore the concept of exploration-exploitation trade-off in reinforcement learning (RL). RL is a machine learning paradigm that involves training an agent to make decisions in complex environments to maximize rewards. The agent must balance exploring new actions and states with exploiting what it already knows to achieve the best outcome.
Think of RL as a hiker navigating through uncharted terrain. The hiker needs to balance exploring new paths with following established trails to avoid getting lost. In RL, the agent must explore new actions and states to gather new experiences, but it also needs to exploit what it already knows to maximize rewards.
We investigate the impact of batch size on the training process. A larger batch size leads to a slower learning process with more accurate results, while a smaller batch size accelerates the learning process at the cost of less accurate results.
Imagine training a model to recognize different images. A bigger batch size is like having more images in each training set, which can help the model learn more accurately. However, it takes longer to train the model, and there may be a limit to how much the model can learn from each training set. On the other hand, a smaller batch size is like training the model on a single image at a time, which accelerates the learning process but may not lead to as accurate results.
We evaluate the quality of the trained models using two metrics: average rewards and loss function. We observe that both metrics follow a similar trend, with initial improvement followed by stabilization. The L2 model shows a significant increase in rewards between 40,000 and 100,000 episodes, indicating improved learning.
Visualizing the evolution of average rewards for charging operations, we see that the L1 model exhibits a temporary worsening of rewards around 150,000 episodes before stabilizing at 160,000 episodes. This behavior may be due to complex environmental conditions causing temporary performance degradation until adaptation.
In conclusion, exploration-exploitation trade-off is a crucial aspect of reinforcement learning. Batch size selection affects the balance between accuracy and speed in training. By evaluating average rewards and loss function, we can determine the quality of the trained models. Understanding these concepts enables us to optimize RL algorithms for complex environments.