Learning to Control Multi- Dimensional Autonomous Agents using Hebbian Learning

Abstract

The novelty-raahn algorithm has been shown to effectively learn a desired behavior from raw inputs by connecting an autoencoder with a Hebbian network. Hebbian learning is compelling for its biological plausibility and simplicity. It changes the weight of a connection based only on the activations of neurons it connects, and can effectively reinforce good behaviors when combined with neuromodulation. These low-level synaptic weight changes make for a better merge of the three learning tasks of perception, prediction and action. However, the state-ofthe art algorithm requires the design of a highly detailed modulation scheme designed for a specific system, which is disconnected from the overall objective it optimizes. In this thesis, we will propose that similar learning behavior can be achieved, by making the autonomous agent react to longer-term rewards, and thus implicitly introducing prediction capabilities. In doing so, the required modulation scheme becomes connected to the global optimization objective.