Mastering MuZero: A General Algorithm for Expert Control with Python

Mastering MuZero: A General Algorithm for Expert Control with Python

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MuZero is a general algorithm for mastering various Atari games using deep reinforcement learning. It defies the need for human demonstration data or a reward function by learning from raw pixel inputs. In this post, we'll discuss MuZero's architecture and explore how it achieves superior performance through self-play.

MuZero starts with a value network which estimates the Q-value for a given state, and a policy network producing a probability distribution over the actions given a state. After some exploration, MuZero uses self-play to learn by operating the game environment with its own policy and observing the rewards. The algorithm uses a "perfect simulation" of the game for offline bootstrapping, creating a target value estimation for each training example.

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