326 citations · 1k across the 67 of their papers we have counts for
6 papers · 1 filter
Exploration for Multi-task Reinforcement Learning with Deep Generative Models
Sai Praveen Bangaru, JS Suhas, Balaraman Ravindran
Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as , ,…
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran +1
Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich f…
HEMI: Hyperedge Majority Influence Maximization
Varun Gangal, Balaraman Ravindran, Ramasuri Narayanam
In this work, we consider the problem of influence maximization on a hypergraph. We first extend the Independent Cascade (IC) model to hypergraphs, and prove that the traditional i…
Linear Bandit algorithms using the Bootstrap
Nandan Sudarsanam, Balaraman Ravindran
This study presents two new algorithms for solving linear stochastic bandit problems. The proposed methods use an approach from non-parametric statistics called bootstrapping to cr…
Dynamic Frame skip Deep Q Network
Aravind Srinivas, Sahil Sharma, Balaraman Ravindran
Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters…
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering
Aravind Srinivas, Ramnandan Krishnamurthy, Peeyush Kumar +1
This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstrac…