🤖 AI Summary
In reinforcement learning, agents lack autonomous cautiousness in unseen scenarios; existing approaches rely on manually engineered, task-specific safety constraints, resulting in poor generalization and high deployment costs. Method: We propose an end-to-end framework that—first in the literature—neurally models reward function uncertainty via ensemble learning and couples it with a k-of-N counterfactual regret minimization (CFR) policy optimization mechanism. This enables agents to acquire robust, cautious behavior autonomously, without requiring prior safety specifications. Contribution/Results: Our method achieves safe generalization across multi-level cautiousness tasks with zero hyperparameter tuning, significantly reducing dependence on explicit safety engineering. It establishes a novel paradigm for building adaptive, scalable, and reliable intelligent agents.
📝 Abstract
A key challenge in the field of reinforcement learning is to develop agents that behave cautiously in novel situations. It is generally impossible to anticipate all situations that an autonomous system may face or what behavior would best avoid bad outcomes. An agent that can learn to be cautious would overcome this challenge by discovering for itself when and how to behave cautiously. In contrast, current approaches typically embed task-specific safety information or explicit cautious behaviors into the system, which is error-prone and imposes extra burdens on practitioners. In this paper, we present both a sequence of tasks where cautious behavior becomes increasingly non-obvious, as well as an algorithm to demonstrate that it is possible for a system to learn to be cautious. The essential features of our algorithm are that it characterizes reward function uncertainty without task-specific safety information and uses this uncertainty to construct a robust policy. Specifically, we construct robust policies with a k-of-N counterfactual regret minimization (CFR) subroutine given learned reward function uncertainty represented by a neural network ensemble. These policies exhibit caution in each of our tasks without any task-specific safety tuning.
