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Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. One of the primary challenges is detection and recognition of objects in the presence of transformations such as resolution, rotation, translation, scale and occlusion. In this work, we investigate a biologically-inspired computational modeling approach that exploits reinforcement learning (RL) for transformation-invariant image recognition. The RL is implemented in an adaptive critic design (ACD) framework to approximate the neuro-dynamic programming. Two ACD algorithms such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) are investigated and compared for transformation invariant recognition. The two learning algorithms are evaluated statistically using simulated transformations in 2-D images as well as with a large-scale UMIST 2-D face database with pose variations. Our simulations show promising results for both HDP and DHP for transformation-invariant image recognition as well as face authentication. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. On the other hand, HDP is more robust than DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational complexity involved in HDP is much less.