On the Relationship Between Active Inference and Control as Inference
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Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence. Control-as-Inference (CAI) is a framework within reinforcement learning which casts decision making as a variational inference problem. While these frameworks both consider action selection through the lens of variational inference, their relationship remains unclear. Here, we provide a formal comparison between them and demonstrate that the primary difference arises from how value is incorporated into their respective generative models. In the context of this comparison, we highlight several ways in which these frameworks can inform one another.
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Cited by 2 Pith papers
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Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds
Mind Dreamer adds an adversarial generator for non-continuous latent starting states plus Relay Value and Uncertainty Functions to handle credit assignment across jumps, claiming 1.67x average and 8.8x sparse-reward s...
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Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds
Mind Dreamer uses active causal intervention via an adversarial initial-state generator and relay value functions to untether imagination in MBRL, claiming 1.67x average and up to 8.8x sparse-reward speedups over DreamerV3.
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