A Novel Model for Arbitration between Planning and Habitual Control Systems

TL;DR

This paper introduces an integrated arbitration model combining habitual and deliberate control systems, demonstrating rapid learning and robustness in a robotic target-reaching task, especially under changing conditions and occlusion.

Contribution

It presents a novel architecture with an arbitrator that dynamically switches between habitual and planning control, improving adaptability and learning speed in robotic tasks.

Findings

The model learns system kinematics rapidly without prior knowledge.

It maintains performance despite environmental changes and occlusion.

The integrated system outperforms pure habitual or planning models.

Abstract

It is well established that humans decision making and instrumental control uses multiple systems, some which use habitual action selection and some which require deliberate planning. Deliberate planning systems use predictions of action-outcomes using an internal model of the agent's environment, while habitual action selection systems learn to automate by repeating previously rewarded actions. Habitual control is computationally efficient but may be inflexible in changing environments. Conversely, deliberate planning may be computationally expensive, but flexible in dynamic environments. This paper proposes a general architecture comprising both control paradigms by introducing an arbitrator that controls which subsystem is used at any time. This system is implemented for a target-reaching task with a simulated two-joint robotic arm that comprises a supervised internal model and deep reinforcement learning. Through permutation of target-reaching conditions, we demonstrate that the proposed is capable of rapidly learning kinematics of the system without a priori knowledge, and is robust to (A) changing environmental reward and kinematics, and (B) occluded vision. The arbitrator model is compared to exclusive deliberate planning with the internal model and exclusive habitual control instances of the model. The results show how such a model can harness the benefits of both systems, using fast decisions in reliable circumstances while optimizing performance in changing environments. In addition, the proposed model learns very fast. Finally, the system which includes internal models is able to reach the target under the visual occlusion, while the pure habitual system is unable to operate sufficiently under such conditions.