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- Open Access
Reinforcement learning on complex visual stimuli
BMC Neuroscience volume 10, Article number: P90 (2009)
Animals are confronted with the problem of initiating motor actions based on very complex sensory input. We have built a biologically plausible model that uses reinforcement learning on complex visual stimuli to direct an agent towards a target. This is made possible by first extracting a high-level representation of the scene with a hierarchical network and then applying a correlation based RL-learning rule.
The sensory input given to the model consists of grayscale images of size 155 × 155 pixels; see figure. Given this complex input, the model should extract the position and direction of the agent, and the position of the target. This estimation is successfully performed by a multi-layer hierarchical network modeled after the visual system . In each layer, we use Slow Feature Analysis (SFA) [2, 3] to efficiently extract higher-level features based on time structure. SFA has the advantage that learning is done unsupervised, just by feeding the model with image sequences. The high-level output of the hierarchical network is then used to learn corresponding motor commands with a reinforcement-learning algorithm. The reward signal is given by the distance to the target, which is the only supervision signal in the whole model (biologically it could be interpreted as a scent of the target). The motor command output is then used to update the scene, so the model runs in a feedback loop. The resulting trajectories (Figure 1) show how the model directs the agent towards its target. Our model demonstrates that by a division-of-labor strategy simple learning rules can solve a rather difficult problem.
Franzius M, Wilbert N, Wiskott L: Invariant object recognition with slow feature analysis. Proc 18th Int'l Conf on Artificial Neural Networks. Edited by: Kurková V, Neruda R, Koutník J. 2008, Springer-Verlag, 961-970.
Wiskott L, Sejnowski TJ: Slow feature analysis: Unsupervised learning of invariances. Neural Computation. 2002, 14: 715-770. 10.1162/089976602317318938.
Zito T, Wilbert N, Wiskott L, Berkes P: Modular toolkit for data processing (MDP): A Python data processing framework. Front Neuroinformatics. 2008, 2: 8.