45 citations · 145 across the 15 of their papers we have counts for
5 papers · 1 filter
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning
Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor
Some reinforcement learning methods suffer from high sample complexity causing them to not be practical in real-world situations. -function reuse, a transfer learning method, is…
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Yaodong Yang, Jun Luo, Ying Wen +5
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum f…
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
Matthew E. Taylor, Nicholas Nissen, Yuan Wang +1
Reinforcement learning (RL) is a popular machine learning paradigm for game playing, robotics control, and other sequential decision tasks. However, RL agents often have long learn…
Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Manan Tomar, Amy Zhang, Roberto Calandra +2
Accuracy and generalization of dynamics models is key to the success of model-based reinforcement learning (MBRL). As the complexity of tasks increases, so does the sample ineffici…
Partially Observable Mean Field Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley +1
Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agen…