Bidirectional Interaction between Visual and Motor Generative Models using Predictive Coding and Active Inference

Instead, supervision can be available In this work, we tackle the problem of motor in the shape of desired sensory observations, for instance sequence learning for an embodied agent. A provided by a teaching agent. In the case wide range of approaches have been proposed of handwriting, these desired sensory observations to model sequential data, using various types of are visual observations of the target letters. In reinforcement neural architectures (Recurrent Neural Networks learning, the preference for certain sensory (RNNs), Long Short-Term Memories (LSTMs) states is modeled by assigning rewards to the [1], Restricted Boltzmann Machines (RBMs) [2]) desired states, and the agent learns a behavioral and various learning strategies (backpropagation policy maximizing its expected return (sum of rewards) through time (BPTT), Real-Time Recurrent over time. Alternatively, Active Inference Learning (RTRL) [3], Reservoir Computing (RC) (AIF) [6, 7], derived from the Free Energy Principle [4, 5]).