172 citations · 312 across the 7 of their papers we have counts for
4 papers · 1 filter
Characterizing the Gap Between Actor-Critic and Policy Gradient
Junfeng Wen, Saurabh Kumar, Ramki Gummadi +1
Actor-critic (AC) methods are ubiquitous in reinforcement learning. Although it is understood that AC methods are closely related to policy gradient (PG), their precise connection…
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning
Saurabh Kumar, Pararth Shah, Dilek Hakkani-Tur +1
We present a framework combining hierarchical and multi-agent deep reinforcement learning approaches to solve coordination problems among a multitude of agents using a semi-decentr…
Learning to Compose Skills
Himanshu Sahni, Saurabh Kumar, Farhan Tejani +1
We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills. By recursively composing skills with themselves, we…
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning
Himanshu Sahni, Saurabh Kumar, Farhan Tejani +2
Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even t…