765 citations · 1.5k across the 62 of their papers we have counts for
19 papers · 1 filter
Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment
Chris Beeler, Xinkai Li, Colin Bellinger +3
Using a novel toy nautical navigation environment, we show that dynamic programming can be used when only incomplete information about a partially observed Markov decision process…
Decentralized Mean Field Games
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley +1
Multiagent reinforcement learning algorithms have not been widely adopted in large scale environments with many agents as they often scale poorly with the number of agents. Using m…
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning
Colin Bellinger, Andriy Drozdyuk, Mark Crowley +1
The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing. A major challenge, however, lies in fact that me…
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial autoencoders, and their variants. We start with explaining adversarial learning and the van…
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
Ken Ming Lee, Sriram Ganapathi Subramanian, Mark Crowley
Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported go…
Multi-Agent Advisor Q-Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and sl…