2 citations · 3 across the 13 of their papers we have counts for
8 papers · 1 filter
Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networks
Wesley A Suttle, Vipul K Sharma, Brian M Sadler
Multi-agent reinforcement learning (MARL) methods typically require that agents enjoy global state observability, preventing development of decentralized algorithms and limiting sc…
Value of Information-based Deceptive Path Planning Under Adversarial Interventions
Wesley A. Suttle, Jesse Milzman, Mustafa O. Karabag +2
Existing methods for deceptive path planning (DPP) address the problem of designing paths that conceal their true goal from a passive, external observer. Such methods do not apply…
Direct Preference Optimization for Primitive-Enabled Hierarchical RL: A Bilevel Approach
Utsav Singh, Souradip Chakraborty, Wesley A. Suttle +6
Hierarchical reinforcement learning (HRL) enables agents to solve complex, long-horizon tasks by decomposing them into manageable sub-tasks. However, HRL methods face two fundament…
DIPPER: Direct Preference Optimization to Accelerate Primitive-Enabled Hierarchical Reinforcement Learning
Utsav Singh, Souradip Chakraborty, Wesley A. Suttle +3
Learning control policies to perform complex robotics tasks from human preference data presents significant challenges. On the one hand, the complexity of such tasks typically requ…
PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling
Utsav Singh, Wesley A. Suttle, Brian M. Sadler +2
In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based…
Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems
Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju +4
We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of co…
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
Bhrij Patel, Wesley A. Suttle, Alec Koppel +4
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 need…
Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks
Michael Y. Fatemi, Wesley A. Suttle, Brian M. Sadler
Deceptive path planning (DPP) is the problem of designing a path that hides its true goal from an outside observer. Existing methods for DPP rely on unrealistic assumptions, such a…