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Durgesh Kalwar

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2 papers

1 author row

AAMAS Conference 2026 Conference Paper

Nested Training for Mutual Adaptation in Human-AI Teaming

Upasana Biswas
Durgesh Kalwar
Subbarao Kambhampati
Sarath Sreedharan

Mutual adaptation is essential in human–robot teaming, as humans adjust their behavior in response to the robot. Prior work trains against diverse but static partners, missing adaptive human responses, while simultaneous multi-agent learning often yields brittle coordination conventions that fail to generalize. We model human–robot teaming as a finite-Level Interactive Partially Observable Markov Decision Process (I-POMDP), explicitly representing human adaptation within the state. To approximately solve this formulation, we introduce a nested training regime in which agents at a level are trained against adaptive agents at a level below. This exposes agents to adaptation while preventing emergence of opaque coordination strategies. In the Overcooked domain with required-cooperation, our method outperforms standard baselines with unseen adaptive partners and demonstrates stronger adaptability during interaction.

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AAMAS Conference 2023 Conference Paper

Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning

Durgesh Kalwar
Omkar Shelke
Somjit Nath
Hardik Meisheri
Harshad Khadilkar

Improving sample efficiency is a key challenge in reinforcement learning, especially in environments with large state spaces and sparse rewards. In literature, this is resolved either through the use of auxiliary tasks (subgoals) or through clever exploration strategies. Exploration methods have been used to sample better trajectories in large environments while auxiliary tasks have been incorporated where the reward is sparse. However, few studies have attempted to tackle both large scale and reward sparsity at the same time. This paper explores the idea of combining exploration with auxiliary task learning using General Value Functions (GVFs) and a directed exploration strategy. We present a way to learn value functions which can be used to sample actions and provide directed exploration. Experiments on navigation tasks with varying grid sizes demonstrate the performance advantages over several competitive baselines.

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