An Online Data-Driven Emergency-Response Method for Autonomous Agents in Unforeseen Situations

TL;DR

This paper introduces an online, data-efficient emergency-response method for autonomous agents, enabling quick adaptation to unforeseen situations by minimizing auto-encoder reconstruction error, demonstrated in a simulated driving scenario.

Contribution

It presents a novel online Bayesian optimization approach that allows autonomous agents to respond to unexpected events with minimal data, enhancing robustness in unforeseen situations.

Findings

Agent responds to unseen objects within 2 seconds.

Method requires only about 30 data points for adaptation.

Effective in simulated 3D car driving scenario.

Abstract

Reinforcement learning agents perform well when presented with inputs within the distribution of those encountered during training. However, they are unable to respond effectively when faced with novel, out-of-distribution events, until they have undergone additional training. This paper presents an online, data-driven, emergency-response method that aims to provide autonomous agents the ability to react to unexpected situations that are very different from those it has been trained or designed to address. In such situations, learned policies cannot be expected to perform appropriately since the observations obtained in these novel situations would fall outside the distribution of inputs that the agent has been optimized to handle. The proposed approach devises a customized response to the unforeseen situation sequentially, by selecting actions that minimize the rate of increase of the reconstruction error from a variational auto-encoder. This optimization is achieved online in a data-efficient manner (on the order of 30 data-points) using a modified Bayesian optimization procedure. We demonstrate the potential of this approach in a simulated 3D car driving scenario, in which the agent devises a response in under 2 seconds to avoid collisions with objects it has not seen during training.