Noisy Neural Networks for Energy Management: A Reinforcement Learning Approach

In this article, we explore the concept of "SAC" (Soft Actor-Critic) algorithm in the context of reinforcement learning. SAC is a type of off-policy algorithm that combines the benefits of both policy-based and value-based methods. The algorithm learns both the policy and the value function simultaneously, allowing it to adapt more quickly to changing environments.
The SAC algorithm is based on the idea of maximum entropy RL, which means that the algorithm tries to maximize the entropy of the policy while also maximizing the expected return. This approach encourages exploration and leads to a more diverse set of policies.
To update the value function, SAC uses a noisy network that incorporates noise into the neural network’s weights and biases. This helps the algorithm to avoid getting stuck in local optima and improve its overall performance.
We also introduce parameter noises to enhance exploration, which helps the algorithm to explore more efficiently and learn more effectively.
Overall, SAC is a powerful algorithm that can be used in a variety of applications, including robotics, game playing, and autonomous driving. By combining the benefits of both policy-based and value-based methods, SAC provides a more comprehensive approach to reinforcement learning than other algorithms.