I. Szita | Searcharxiv

arXiv2006

Mind model seems necessary for the emergence of communication

We consider communication when there is no agreement about symbols and meanings. We treat it within the framework of reinforcement learning. We apply different reinforcement learning models in our studies and simplify the problem as much as possible. We show that the modelling of the other agent is insufficient in the simplest possible case, unless the intentions can also be modelled. The model of the agent and its intentions enable quick agreements about symbol-meaning association. We show that when both agents assume an `intention model' about the other agent then the symbol-meaning association process can be spoiled and symbol meaning association may become hard.

q-bio.NC↗

arXiv2004

Applying Policy Iteration for Training Recurrent Neural Networks

Recurrent neural networks are often used for learning time-series data. Based on a few assumptions we model this learning task as a minimization problem of a nonlinear least-squares cost function. The special structure of the cost function allows us to build a connection to reinforcement learning. We exploit this connection and derive a convergent, policy iteration-based algorithm. Furthermore, we argue that RNN training can be fit naturally into the reinforcement learning framework.

cs.AI↗