arXiv subjects

Srikar Babu Gadipudi

Publications and source records attributed to Srikar Babu Gadipudi.

2 recordsLinked to original sources

arXiv2026

Strategic Arbitrage in Electricity Markets: A Multi-Agent Policy Optimization Framework

Grid-scale storage and load aggregators are growing quickly in wholesale electricity markets. If highly consolidated, these new participants may distort the competitive market equilibrium through strategic withholding behavior. Traditional market power mitigation techniques are ill-suited to arbitrage-based participants. We provide a multi-agent policy optimization model to study game-theoretic equilibria for real-time markets with consolidated storage and/or aggregators. The model is designed to facilitate natural first-order equilibrium-seeking update rules. We analyze the theoretical properties of different supply functions and their implications on the convergence of the gradient dynamics. Finally, we train equilibrium policies for a modified version of the IEEE RTS-GMLC benchmark across different levels of storage fleet consolidation, to demonstrate the potential of the framework as a tool to characterize and quantify strategic arbitrage.

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arXiv2024

OffRIPP: Offline RL-based Informative Path Planning

Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution -- key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach.

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