A policy-conditional world model trained under active inference enables single-lookahead planning over hundreds of steps and beats a DQN baseline on energy-efficient control of parallel machines.
Mastering diverse control tasks through world models
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Deep Active Inference Agents for Delayed and Long-Horizon Environments
A policy-conditional world model trained under active inference enables single-lookahead planning over hundreds of steps and beats a DQN baseline on energy-efficient control of parallel machines.