96 citations · 369 across the 74 of their papers we have counts for
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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…
Actor-Critic or Critic-Actor? A Tale of Two Time Scales
Shalabh Bhatnagar, Vivek S. Borkar, Soumyajit Guin
We revisit the standard formulation of tabular actor-critic algorithm as a two time-scale stochastic approximation with value function computed on a faster time-scale and policy co…
A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization
Akash Mondal, Prashanth L. A., Shalabh Bhatnagar
In this paper, we present a stochastic gradient algorithm for minimizing a smooth objective function that is an expectation over noisy cost samples, and only the latter are observe…
Reinforcement Learning for Task Specifications with Action-Constraints
Arun Raman, Keerthan Shagrithaya, Shalabh Bhatnagar
In this paper, we use concepts from supervisory control theory of discrete event systems to propose a method to learn optimal control policies for a finite-state Markov Decision Pr…