Avoiding Death through Fear Intrinsic Conditioning
Rodney Sanchez, Ferat Sahin, Alexander Ororbia, Jamison Heard

TL;DR
This paper introduces a biologically-inspired intrinsic reward mechanism based on fear conditioning, enabling agents to avoid terminal states like death in complex environments, and models behaviors akin to anxiety disorders.
Contribution
It presents a novel memory-augmented neural network architecture that produces fear-based intrinsic motivation, improving agent behavior in environments with non-descriptive terminal states.
Findings
Agents exhibit avoidance of terminal states similar to fear conditioning.
Modulating fear response thresholds produces behaviors analogous to anxiety disorders.
The framework effectively solves environments with sparse, non-descriptive terminal rewards.
Abstract
Biological and psychological concepts have inspired reinforcement learning algorithms to create new complex behaviors that expand agents' capacity. These behaviors can be seen in the rise of techniques like goal decomposition, curriculum, and intrinsic rewards, which have paved the way for these complex behaviors. One limitation in evaluating these methods is the requirement for engineered extrinsic for realistic environments. A central challenge in engineering the necessary reward function(s) comes from these environments containing states that carry high negative rewards, but provide no feedback to the agent. Death is one such stimuli that fails to provide direct feedback to the agent. In this work, we introduce an intrinsic reward function inspired by early amygdala development and produce this intrinsic reward through a novel memory-augmented neural network (MANN) architecture. We…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMemory and Neural Mechanisms · Reinforcement Learning in Robotics · Social Robot Interaction and HRI
