16 citations · 16 across the 3 of their papers we have counts for
4 papers
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning
Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran
Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent. However, such techniques often yield a…
Successor Options: An Option Discovery Framework for Reinforcement Learning
Rahul Ramesh, Manan Tomar, Balaraman Ravindran
The options framework in reinforcement learning models the notion of a skill or a temporally extended sequence of actions. The discovery of a reusable set of skills has typically e…
AUPCR Maximizing Matchings : Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings
Girish Raguvir J, Rahul Ramesh, Sachin Sridhar +1
We consider the problem of computing a matching in a bipartite graph in the presence of one-sided preferences. There are several well studied notions of optimality which include pa…
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning
Sahil Sharma, Aravind Suresh, Rahul Ramesh +1
Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains. Among all such visual decision making p…