3 papers
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.AI2025
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…