MuZero is a reinforcement learning algorithm that learns to make decisions without needing an explicit model of its environment. Instead of relying on predefined rules or a full understanding of state dynamics, it combines elements of model-based and model-free reinforcement learning. This allows MuZero to train and improve its performance by focusing solely on the task at hand, like playing a game, without needing to be aware of all the details of the game's rules or mechanics.
The core idea behind MuZero's approach is that it learns representations of the environment through experience. It employs a technique known as "self-supervised learning," where the algorithm gathers data from its interactions with the environment. MuZero constructs a model that predicts the outcome of actions based on past observations. For instance, while playing chess, it doesn't know all the rules upfront but learns from playing many games, noticing patterns and outcomes, such as how certain moves lead to winning or losing situations.
MuZero uses this learned model to simulate future states and to improve its decision-making. Instead of needing a full model of the environment, it only needs to guess the next state based on the current state and the action taken. By iteratively refining its predictions through repeated plays, it can learn which actions lead to better outcomes over time. This method allows MuZero to generalize its learnings across similar tasks, leading to effective performance even in environments it has not explicitly been trained on.