4 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
When Can Model-Free Reinforcement Learning be Enough for Thinking?
Josiah P. Hanna, Nicholas E. Corrado
Recent work on large language models has demonstrated the use of model-free reinforcement learning (RL) to train reasoning-like capabilities. The emergence of "thinking" through mo…
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling
Nicholas E. Corrado, Josiah P. Hanna
On-policy reinforcement learning (RL) algorithms are typically characterized as algorithms that perform policy updates using i.i.d. trajectories collected by the agent's current po…
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning
Nicholas E. Corrado, Yuxiao Qu, John U. Balis +2
In offline reinforcement learning (RL), an RL agent learns to solve a task using only a fixed dataset of previously collected data. While offline RL has been successful in learning…
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates
Nicholas E. Corrado, Josiah P. Hanna
Recently, data augmentation (DA) has emerged as a method for leveraging domain knowledge to inexpensively generate additional data in reinforcement learning (RL) tasks, often yield…