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MuZero mirrors hippocampus processing. (Left) Functions of muZero. (Right) Analogous/similar hippocampal functions. (Left top) In muZero, the representation functions maps the initial state, such as the position on a checker board, to an internal hidden state that emphasizes important features of the environment. (Right top) A similar processing in the extrahippocampal system (EHPCs) represents the environment in a cognitive map as a serious of cell positions encoded by grid and place cells. Place cells tend to have fields clustered around the location of a reward, such as the destination of a candy shop. (Left center) From the hidden state, a prediction function computes potential actions (policies) and rewards (values) of the location in the current state. (Right center) In the EHPCs, place cells encode features of the environment in relation to their predictive relationship with other features, such as the potential presence of reward. Additionally, state prediction in HPC (via theta sequences) can be coupled through theta coordination coherence to other areas of the brain that process action and reward. (Bottom left) In muZero, the dynamics function calculates reward (such as a captured checkers piece) and computes a new hidden state. (Bottom right) In the EHPCs, offline replay, which occurs during periods of sleep, emphasizes locations of previous reward. During replay, events can be selectively sampled from visited event space, with priority given to representations or locations that correspond with reward. Reward-predictive representations can then be compressed into a lower dimensional space. Replay can be comprised of novel state configurations that have not been experienced, such as a novel route to the reward location.
In muZero, compression, which occurs during the dynamics function, plays an essential role in the generalization of learning. As previously explained, compression in the hippocampus may occur during hippocampal replay during sleep (Figure 1) (Penagos et al., 2017), and, like in muZero, this compression may be an important component of learning generalization (Vértes and Sahani, 2019). We might expect, therefore, for disruptions in sleep replay to cause deficits in task generalization (Hoel, 2021), and for increased replay (such as sleep sessions between tasks) to enhance learning generalization (Djonlagic et al., 2009). If replay plays an important role in generalization, we also might expect replay sequences and structure to be similar between two environments with the same task rules. Specifically, replay may be structured around task organization and reward-contingent actions, resulting in a clustering of replay around shared action states between two environments. 2b1af7f3a8