16 papers
When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
Adam Labiosa, Josiah P. Hanna
It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning. In this paper, we study the question of…
BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer
Yunfu Deng, Josiah P. Hanna
Sim2real transfer for robot policy learning suffers due to mismatch between simulation and reality. Existing methods typically address each gap in isolation through separate adapta…
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning
Kyle Domico, Jean-Charles Noirot Ferrand, Ryan Sheatsley +3
Attacks on machine learning models have been extensively studied through stateless optimization. In this paper, we demonstrate how a reinforcement learning (RL) agent can learn a n…
Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
Nicholas E. Corrado, Wenyuan Huang, Josiah P. Hanna
Multi-task reinforcement learning (MTRL) aims to train a single agent to efficiently optimize performance across multiple tasks simultaneously. However, jointly optimizing all task…
Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies
Nicholas E. Corrado, Josiah P. Hanna
Independent on-policy policy gradient algorithms are widely used for multi-agent reinforcement learning (MARL) in cooperative and no-conflict games, but they are known to converge…
Abstract Sim2Real through Approximate Information States
Yunfu Deng, Yuhao Li, Josiah P. Hanna
In recent years, reinforcement learning (RL) has shown remarkable success in robotics when a fast and accurate simulator is available for a given task. When using RL and simulation…