12 citations · 20 across the 3 of their papers we have counts for
3 papers
State-Aware Variational Thompson Sampling for Deep Q-Networks
Siddharth Aravindan, Wee Sun Lee
Thompson sampling is a well-known approach for balancing exploration and exploitation in reinforcement learning. It requires the posterior distribution of value-action functions to…
An Analysis of Frame-skipping in Reinforcement Learning
Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat +5
In the practice of sequential decision making, agents are often designed to sense state at regular intervals of time steps, , ignoring state information in between sensi…
Learning to Prune Deep Neural Networks via Reinforcement Learning
Manas Gupta, Siddharth Aravindan, Aleksandra Kalisz +2
This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm for pruning neural networks. Unlike current RL based model compression approaches where feedback is gi…