1 citations · 2 across the 6 of their papers we have counts for
6 papers
Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity
Naman Saxena, Mudit Gaur, Vaneet Aggarwal
Bilevel reinforcement learning (RL) is an important framework within the literature of RL that can be used to formalize various categories of problems, such as meta-learning, hiera…
Sample Complexity Analysis for Constrained Bilevel Reinforcement Learning
Naman Saxena, Vaneet Aggarwal
Several important problem settings within the literature of reinforcement learning (RL), such as meta-learning, hierarchical learning, and RL from human feedback (RL-HF), can be mo…
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion
Aditya Shirwatkar, Naman Saxena, Kishore Chandra +1
A core strength of Model Predictive Control (MPC) for quadrupedal locomotion has been its ability to enforce constraints and provide interpretability of the sequence of commands ov…
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…
Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning
Naman Saxena, Gorantla Sandeep, Pushpak Jagtap
Signal Temporal Logic (STL) is a powerful framework for describing the complex temporal and logical behaviour of the dynamical system. Numerous studies have attempted to employ rei…