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Reinforcement learning agents learn how to complete a specific goal through trial and error. Their actions are guided by virtual rewards that shape its overall strategy or policy. MuZero is essentially a planning algorithm that receives visual input, whether it's an image of a chess board or a still from an Atari video game, and transforms the information into a “hidden state”.
The hidden state constantly changes and is updated based on previous hidden states to predict the next action an agent should take in a game. “At every one of these steps the model predicts the policy (e.g. the move to play), value function (e.g. the predicted winner), and immediate reward (e.g. the points scored by playing a move),” the paper explained.
What’s interesting is that the model seems to achieve state-of-the-art performance across 57 Atari games and matches AlphaZero in playing Go, Chess, and Shogi too. It’s more general than AlphaZero, and doesn’t require explicit knowledge of the rules of the games.
“Crucially, our method does not require any knowledge of the game rules or environment dynamics, potentially paving the way towards the application of powerful learning and planning methods to a host of real world domains for which there exists no perfect simulator,” the paper concluded.
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