22 citations · 69 across the 17 of their papers we have counts for
20 papers
State-Aware Virtual Patient Simulation for Training Psychiatrists in Challenging Clinical Scenarios
Harsh Mohta, C Chandrahas Reddy, Sunhith Reddy Bojja +3
Psychiatrists must be prepared to manage patients exhibiting challenging psychological states, including suicidal ideation, hallucinations, and other high-risk behaviors. However,…
Trust Guided Decision Transformer
Chainesh Gautam, Raghuram Bharadwaj Diddigi, Chandramouli Kamanchi +3
Decision Transformer performance degrades on long rollouts because the conditioning context drifts out of the training distribution. We show that this drift is visible through the…
From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning
Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi
Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapi…
Full-Gradient Successor Feature Representations
Ritish Shrirao, Aditya Priyadarshi, Raghuram Bharadwaj Diddigi
Successor Features (SF) combined with Generalized Policy Improvement (GPI) provide a robust framework for transfer learning in Reinforcement Learning (RL) by decoupling environment…
Generalisation in Multitask Fitted Q-Iteration and Offline Q-learning
Kausthubh Manda, Raghuram Bharadwaj Diddigi
We study offline multitask reinforcement learning in settings where multiple tasks share a low-rank representation of their action-value functions. In this regime, a learner is pro…
CLIP-RL: Aligning Language and Policy Representations for Task Transfer in Reinforcement Learning
Chainesh Gautam, Raghuram Bharadwaj Diddigi
Recently, there has been an increasing need to develop agents capable of solving multiple tasks within the same environment, especially when these tasks are naturally associated wi…
Learning Distinguishable Representations in Deep Q-Networks for Linear Transfer
Sooraj Sathish, Keshav Goyal, Raghuram Bharadwaj Diddigi
Deep Reinforcement Learning (RL) has demonstrated success in solving complex sequential decision-making problems by integrating neural networks with the RL framework. However, trai…
Image Generation from Image Captioning -- Invertible Approach
Nandakishore S Menon, Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi
Our work aims to build a model that performs dual tasks of image captioning and image generation while being trained on only one task. The central idea is to train an invertible mo…
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm
Raghuram Bharadwaj Diddigi, Prateek Jain, Prabuchandran K. J. +1
Learning optimal behavior from existing data is one of the most important problems in Reinforcement Learning (RL). This is known as "off-policy control" in RL where an agent's obje…
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement Learning
P. Parnika, Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda +1
In this work, we consider the problem of computing optimal actions for Reinforcement Learning (RL) agents in a co-operative setting, where the objective is to optimize a common goa…
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks
Shravan Nayak, Chanakya Ajit Ekbote, Annanya Pratap Singh Chauhan +5
We consider the problem of energy management in microgrid networks. A microgrid is capable of generating a limited amount of energy from a renewable resource and is responsible for…
A Convergent Off-Policy Temporal Difference Algorithm
Raghuram Bharadwaj Diddigi, Chandramouli Kamanchi, Shalabh Bhatnagar
Learning the value function of a given policy (target policy) from the data samples obtained from a different policy (behavior policy) is an important problem in Reinforcement Lear…
A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Games
Raghuram Bharadwaj Diddigi, Chandramouli Kamanchi, Shalabh Bhatnagar
We consider the problem of two-player zero-sum games. This problem is formulated as a min-max Markov game in the literature. The solution of this game, which is the min-max payoff,…
Generalized Second Order Value Iteration in Markov Decision Processes
Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi, Shalabh Bhatnagar
Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov Decision Process (MDP). Here, a contract…
Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning
Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda, Prabuchandran K. J. +1
In cooperative stochastic games multiple agents work towards learning joint optimal actions in an unknown environment to achieve a common goal. In many real-world applications, how…
Successive Over Relaxation Q-Learning
Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi, Shalabh Bhatnagar
In a discounted reward Markov Decision Process (MDP), the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem…
An Online Sample Based Method for Mode Estimation using ODE Analysis of Stochastic Approximation Algorithms
Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi, Prabuchandran K. J. +1
One of the popular measures of central tendency that provides better representation and interesting insights of the data compared to the other measures like mean and median is the…
A unified decision making framework for supply and demand management in microgrid networks
Diddigi Raghuram Bharadwaj, Sai Koti Reddy Danda, Krishnasuri Narayanam +1
This paper considers two important problems -- on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of…
Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids
Raghuram Bharadwaj Diddigi, D. Sai Koti Reddy, Shalabh Bhatnagar
We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. Th…
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks
Raghuram Bharadwaj Diddigi, Prabuchandran K. J., Shalabh Bhatnagar
We consider the problem of tracking an intruder using a network of wireless sensors. For tracking the intruder at each instant, the optimal number and the right configuration of se…