1 citations · 1 across the 4 of their papers we have counts for
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Policy Gradient Methods for Non-Markovian Reinforcement Learning
Avik Kar, Siddharth Chandak, Rahul Singh +4
We study policy gradient methods for reinforcement learning in non-Markovian decision processes (NMDPs), where observations and rewards depend on the entire interaction history. To…
High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
Avik Kar, Siddharth Chandak, Rahul Singh +3
We present the first uniform-in-time high-probability bound for SGD under the PL condition, where the gradient noise contains both Markovian and martingale difference components. T…
Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problem
Soumyajit Guin, Shalabh Bhatnagar
In this paper we propose two algorithms in the tabular setting and an algorithm for the function approximation setting for the Stochastic Shortest Path (SSP) problem. SSP problems…
An Actor-Critic Algorithm with Function Approximation for Risk Sensitive Cost Markov Decision Processes
Soumyajit Guin, Vivek S. Borkar, Shalabh Bhatnagar
In this paper, we consider the risk-sensitive cost criterion with exponentiated costs for Markov decision processes and develop a model-free policy gradient algorithm in this setti…