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cs.LG2026
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…
cs.LG2026
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…
cs.LG2024
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…