Multi-Critic Actor Learning: Teaching RL Policies to Act with Style

no code implementations • ICLR 2022 • Siddharth Mysore, George Cheng, Yunqi Zhao, Kate Saenko, Meng Wu

MultiCriticAL is tested in the context of multi-style learning, a special case of MTRL where agents are trained to behave with different distinct behavior styles, and yields up to 45% performance gains over the single-critic baselines and even successfully learns behavior styles in cases where single-critic approaches may simply fail to learn.

On Multi-Agent Learning in Team Sports Games

no code implementations • 25 Jun 2019 • Yunqi Zhao, Igor Borovikov, Jason Rupert, Caedmon Somers, Ahmad Beirami

In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II.

reinforcement-learning Reinforcement Learning (RL)

Winning Isn't Everything: Enhancing Game Development with Intelligent Agents

no code implementations • 25 Mar 2019 • Yunqi Zhao, Igor Borovikov, Fernando De Mesentier Silva, Ahmad Beirami, Jason Rupert, Caedmon Somers, Jesse Harder, John Kolen, Jervis Pinto, Reza Pourabolghasem, James Pestrak, Harold Chaput, Mohsen Sardari, Long Lin, Sundeep Narravula, Navid Aghdaie, Kazi Zaman

We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multi-faceted concepts with practical implications outlined in this paper.