Aarokira 1 File

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In benchmark tests on the Dark Room Navigation and Memory Maze tasks, Aarokira-1 outperformed DreamerV3 and Recurrent DQN by an average of 32% in sample efficiency and 41% in final reward convergence. Ablation studies confirm that the intrinsic compression reward is critical for escaping local information traps. We release the code and pretrained models at [anonymous repo]. The Arokira 1 is a product of the

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We introduce Aarokira-1 , a hybrid reinforcement learning (RL) architecture designed to address the challenge of partial observability in non-Markovian environments. Unlike standard POMDP solvers that rely on belief state inference, Aarokira-1 integrates three components: (1) a sparse attention memory module for long-term temporal dependencies, (2) a meta-learning policy that adapts its exploration rate based on uncertainty estimation, and (3) an intrinsic reward signal derived from prediction error compression.