Human-Inspired Pavlovian and Instrumental Learning for Autonomous Agent Navigation
This addresses the challenge of balancing fast responses with goal-directed planning for autonomous agents operating in uncertain environments, representing an incremental improvement over existing RL methods.
The paper tackles the problem of autonomous agent navigation in uncertain environments by proposing a human-inspired hybrid reinforcement learning architecture that integrates Pavlovian, model-free, and model-based components. Simulation results show the approach accelerates learning, improves operational safety, and reduces navigation in high-uncertainty regions compared to standard RL baselines.
Autonomous agents operating in uncertain environments must balance fast responses with goal-directed planning. Classical MF RL often converges slowly and may induce unsafe exploration, whereas MB methods are computationally expensive and sensitive to model mismatch. This paper presents a human-inspired hybrid RL architecture integrating Pavlovian, Instrumental MF, and Instrumental MB components. Inspired by Pavlovian and Instrumental learning from neuroscience, the framework considers contextual radio cues, here intended as georeferenced environmental features acting as CS, to shape intrinsic value signals and bias decision-making. Learning is further modulated by internal motivational drives through a dedicated motivational signal. A Bayesian arbitration mechanism adaptively blends MF and MB estimates based on predicted reliability. Simulation results show that the hybrid approach accelerates learning, improves operational safety, and reduces navigation in high-uncertainty regions compared to standard RL baselines. Pavlovian conditioning promotes safer exploration and faster convergence, while arbitration enables a smooth transition from exploration to efficient, plan-driven exploitation. Overall, the results highlight the benefits of biologically inspired modularity for robust and adaptive autonomous systems under uncertainty.