That’s right , today we’re going to talk about the surprising benefits of using fixed actions and observations in reinforcement learning.

First off, let me explain what these terms mean for those who might not know. In reinforcement learning (RL), an agent is trained to make decisions based on its environment by receiving rewards or punishments for each action it takes. The goal is to find the best sequence of actions that will lead to the highest cumulative reward over time.

Now, in traditional RL algorithms like Q-learning and SARSA, the agent learns a policy function that maps states to actions based on its experiences. This means that for each new state it encounters, the agent has to decide what action to take based on its current knowledge of the environment. But sometimes, this can be overwhelmingly complex or time-consuming.

That’s where fixed actions and observations come in. Instead of learning a policy function from scratch, we can predefine certain actions that will always result in the same reward for each state. This is called a deterministic policy. Similarly, instead of having to process every possible observation, we can choose specific features or variables that are most relevant to our task and ignore the rest.

At first glance, this might seem like cheating or taking the easy way out. But in reality, it’s actually quite clever. By simplifying the problem space, we can reduce the amount of data needed for training and improve the efficiency of learning. This is especially useful when dealing with large state spaces or complex environments where traditional RL algorithms might struggle to converge.

Fixed actions and observations also have some other surprising benefits that you might not expect. For example:

– They can help us avoid overfitting by reducing the number of parameters we need to learn. This is because fixed policies are less prone to memorizing specific state-action pairs, which can lead to poor generalization performance in new environments.

– They can improve the stability and robustness of our agents by making them more predictable and reliable. By using a deterministic policy, we eliminate any randomness or noise that might be introduced during learning, which can help us avoid getting stuck in local optima or oscillating between different solutions.

– They can also reduce the computational cost of training and testing our agents by eliminating the need for expensive function approximations or complex optimization algorithms. This is because fixed policies are much simpler to implement than traditional RL algorithms, which can be computationally intensive and time-consuming.

By using fixed actions and observations, we can simplify our problem space, reduce overfitting, improve stability and robustness, and save computational resources. Who knew that sometimes, doing less could actually be more?