How does the Monte Carlo method estimate the value of a state or state-action pair in reinforcement learning?
The Monte Carlo (MC) method is a fundamental approach in the field of reinforcement learning (RL) for estimating the value of states or state-action pairs. This method is particularly useful in model-free prediction and control, where the underlying dynamics of the environment are not known. The Monte Carlo method leverages the power of repeated random
What is the main advantage of model-free reinforcement learning methods compared to model-based methods?
Model-free reinforcement learning (RL) methods have gained significant attention in the field of artificial intelligence due to their unique advantages over model-based methods. The primary advantage of model-free methods lies in their ability to learn optimal policies and value functions without requiring an explicit model of the environment. This characteristic provides several benefits, including reduced