How does MuZero learn without knowing the environment?

MuZero learns to operate in unknown environments by building and refining an internal model of the environment through interaction. Unlike traditional reinforcement learning (RL) methods that rely on predefined rules or dynamics, MuZero uses neural networks to predict three key elements: the state of the environment, the rewards for actions, and the policy (i.e., which actions are promising). These predictions are made by a combination of a representation network (which encodes observations into a latent state), a dynamics network (which predicts future latent states and rewards), and a prediction network (which estimates the policy and value for a state). By training these networks to minimize prediction errors, MuZero effectively constructs its own understanding of the environment’s behavior without explicit prior knowledge.

For example, when learning to play a game like Atari Breakout, MuZero doesn’t receive information about the physics of the ball or paddle. Instead, it observes pixels from the screen and uses trial and error to infer how actions (e.g., moving the paddle left/right) affect the game state. During training, MuZero simulates hypothetical future trajectories using its internal model. It selects actions that maximize predicted rewards by balancing exploration (trying new actions) and exploitation (leveraging known strategies). Over time, the model improves by comparing its predictions (e.g., “the ball will bounce at this angle”) to actual outcomes, adjusting its neural networks via gradient descent to reduce discrepancies.

The key innovation is that MuZero decouples environment dynamics from planning. Even without knowing the true rules, the learned model enables planning via Monte Carlo Tree Search (MCTS). MCTS uses the dynamics and prediction networks to simulate possible future steps, evaluate their outcomes, and choose optimal actions. This approach allows MuZero to handle complex, partially observable environments—such as video games or robotic control tasks—by continuously refining its internal model. The model’s accuracy improves as more data is collected, making the system adaptable to diverse scenarios. Developers can apply similar principles to other domains by training neural networks to predict state transitions and rewards, then integrating them with planning algorithms like MCTS for decision-making.