Krishna Chaitanya Kalagarla
Orcid: 0000-0003-0618-8342
According to our database1,
Krishna Chaitanya Kalagarla
authored at least 14 papers
between 2020 and 2025.
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Bibliography
2025
Optimal Control of Logically Constrained Partially Observable and Multiagent Markov Decision Processes.
IEEE Trans. Autom. Control., January, 2025
Proceedings of the 28th ACM International Conference on Hybrid Systems: Computation and Control, 2025
A Safe Bayesian Learning Algorithm for Constrained MDPs with Bounded Constraint Violation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025
2024
Compositional Planning for Logically Constrained Multi-Agent Markov Decision Processes.
Proceedings of the 63rd IEEE Conference on Decision and Control, 2024
2023
Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes.
CoRR, 2023
CoRR, 2023
2022
Optimal control of partially observable Markov decision processes with finite linear temporal logic constraints.
Proceedings of the Uncertainty in Artificial Intelligence, 2022
Practical Control Design for the Deep Learning Age: Distillation of Deep RL-Based Controllers.
Proceedings of the 58th Annual Allerton Conference on Communication, 2022
2021
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications.
CoRR, 2021
Model-Free Reinforcement Learning for Optimal Control of Markov Decision Processes Under Signal Temporal Logic Specifications.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
Optimal Control of Discounted-Reward Markov Decision Processes Under Linear Temporal Logic Specifications.
Proceedings of the 2021 American Control Conference, 2021
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021
2020
Synthesis of Discounted-Reward Optimal Policies for Markov Decision Processes Under Linear Temporal Logic Specifications.
CoRR, 2020
Designing Interpretable Approximations to Deep Reinforcement Learning with Soft Decision Trees.
CoRR, 2020