To build artificial agents that can understand the environment around them, the semantics of their internal state should be learnable to them. Since neurons embedded in the brain cannot access directly to the outside, motor interaction with the environment is necessary. Sensory invariance driven action (SIDA) has been proposed [1] to learn the meaning of internal state based on reinforcement learning as shown in Figure 1. However, SIDA uses fixed Gabor filters to interact with the environment. It is possible that the receptive fields (RFs) in SIDA can also be learned based on the simultaneous statistics, and not just the state-action mapping. We propose the use of adaptive filters based on independent component analysis (ICA) in the SIDA framework for receptive field learning.

Figure 2 shows the two types of filters and their reward tables with different primitives. We can see that the adaptive filters are more edge-like as in the input image than the Gabor filters. Also the values with Gabor filters are 0.6379 and 0.7808, while ones with the adaptive filters are 0.7091 and 0.9907. This means our proposed method better understand the environment than original SIDA.