96 citations · 369 across the 74 of their papers we have counts for
83 papers
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks
Arunselvan Ramaswamy, Shalabh Bhatnagar, Naman Saxena
We present a novel algorithm for training deep neural networks in supervised (classification and regression) and unsupervised (reinforcement learning) scenarios. This algorithm com…
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
Naman Saxena, Subhojyoti Khastigir, Shishir Kolathaya +1
The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few rece…
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning
Mizhaan Prajit Maniyar, Akash Mondal, Prashanth L. A. +1
We consider the problem of control in the setting of reinforcement learning (RL), where model information is not available. Policy gradient algorithms are a popular solution approa…
n-Step Temporal Difference Learning with Optimal n
Lakshmi Mandal, Shalabh Bhatnagar
We consider the problem of finding the optimal value of n in the n-step temporal difference (TD) learning algorithm. Our objective function for the optimization problem is the aver…
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
Ashish Kumar Jayant, Shalabh Bhatnagar
During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform a significant number of random exploratory steps. In the real world, this can l…
A policy gradient approach for Finite Horizon Constrained Markov Decision Processes
Soumyajit Guin, Shalabh Bhatnagar
The infinite horizon setting is widely adopted for problems of reinforcement learning (RL). These invariably result in stationary policies that are optimal. In many situations, fin…