AIConfPaper
← Search

Wesley A Suttle

7 accepted papers

2026

Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach

Utsav Singh, Souradip Chakraborty, Wesley A. Suttle, Brian M. Sadler, Derrik E. Asher, Anit Kumar Sahu +3
ICLR 2026poster

Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundamental challenges: (i) non-stationarity caused by the evolving lower-level policy during training, which destabilizes higher…

Cited by 0Source ↗Scholar ↗
2025

Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning

Wesley A. Suttle, Aamodh Suresh, Carlos Nieto-Granda
ICLR 2025poster

Entropy-based objectives are widely used to perform state space exploration in reinforcement learning (RL) and dataset generation for offline RL. Behavioral entropy (BE), a rigorous generalization of classical entropies that incorporates cognitive and perceptual biases of agents, was recently propos…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation

Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Tianyi Zhou +2
IROS 2025

Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. However, real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and suboptimal policies due to sample inefficiency of RL. In this work,

Cited by 4Source ↗Scholar ↗
2025

Globally Optimal Policy Gradient Algorithms for Reinforcement Learning with PID Control Policies

Vipul Kumar Sharma, Wesley A. Suttle, S Sivaranjani
NeurIPS 2025poster

We develop policy gradient algorithms with global optimality and convergence guarantees for reinforcement learning (RL) with proportional-integral-derivative (PID) parameterized control policies. RL enables learning control policies through direct interaction with a system, without explicit model kn…

Cited by 0Source ↗Scholar ↗
2024

LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments

Chak Lam Shek, Xiyang Wu, Wesley A. Suttle, Carl Busart, Erin Zaroukian, Dinesh Manocha +2
IROS 2024poster

Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context from their observations, creating a similar context-aware navigation system for robots is difficult. The essence of the iss…

Cited by 11Source ↗Scholar ↗
2024

PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling

Utsav Singh, Wesley A Suttle, Brian M. Sadler, Vinay P. Namboodiri, Amrit Bedi
ICML 2024poster

In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward model to relabel higher-level replay buffers. Since…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles

Bhrij Patel, Wesley A Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Dinesh Manocha +1
ICML 2024poster

In the context of average-reward reinforcement learning, the requirement for oracle knowledge of the mixing time, a measure of the duration a Markov chain under a fixed policy needs to achieve its stationary distribution, poses a significant challenge for the global convergence of policy gradient me…

Cited by 2Source ↗PDF ↗Scholar ↗