Yoonjae Jung

Sang-Hyun Lee, Yoonjae Jung, Seung-Woo Seo

Hierarchical reinforcement learning (HRL) incorporates temporal abstraction into reinforcement learning (RL) by explicitly taking advantage of hierarchical structure. Modern HRL typically designs a hierarchical agent composed of a high-level policy and low-level policies. The high-level policy selects which low-level policy to activate at a lower frequency and the activated low-level policy selects an action at each time step. Recent HRL algorithms have achieved performance gains over standard RL algorithms in synthetic navigation tasks. However, we cannot apply these HRL algorithms to real-world navigation tasks. One of the main challenges is that real-world navigation tasks require an agent to perform safe and interactive behaviors in dynamic environments. In this paper, we propose imagination-augmented HRL (IAHRL) that efficiently integrates imagination into HRL to enable an agent to learn safe and interactive behaviors in real-world navigation tasks. Imagination is to predict the consequences of actions without interactions with actual environments. The key idea behind IAHRL is that the low-level policies imagine safe and structured behaviors, and then the high-level policy infers interactions with surrounding objects by interpreting the imagined behaviors. We also introduce a new attention mechanism that allows our high-level policy to be permutation-invariant to the order of surrounding objects and to prioritize our agent over them. To evaluate IAHRL, we introduce five complex urban driving tasks, which are among the most challenging real-world navigation tasks. The experimental results indicate that IAHRL enables an agent to perform safe and interactive behaviors, achieving higher success rates and lower average episode steps than baselines.

Minjae Lee, Yoonjae Jung, Sangdon Park

Large language generative models increasingly interact with humans, while their falsified responses raise concerns. To mitigate this hallucination effect, selectively abstaining from answering, called selective generation, provides an effective way for generators to control the hallucination when uncertain about their answers. However, as selective generators interact under adversarial environments and receive partial feedback from users on selected generation (e.g., thumbs up or down on the selected answer), learning methods for selective generation under such practical setups are crucial but currently missing. To address this limitation, we propose an online learning algorithm for selective generation with partial feedback under an adaptive adversary. In particular, we re-purpose an adversarial bandit algorithm to design an online selective generation method with controllable false discovery rates (FDR), which measures the rate of hallucination. The key building blocks include a novel conversion lemma from regret of any bandit algorithm to the FDR, and the exploitation of a unique structure of selective generation to reuse partial feedback, which we call feedback unlocking. We empirically evaluate the efficacy of the proposed online selective generation algorithm with partial feedback over diverse learning environments, demonstrating its ability to control the FDR, while maintaining reasonable selection efficiency, i.e., the ratio of non-abstaining answers, compared to baselines.