CV

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Interests

Artificial Intelligence, Reinforcement Learning, Inverse Reinforcement Learning, Real-time Planning, Robotics

Education

• Ph.D in Computer Science, University of Massachusetts, Amherst, 2025 (expected).
  • Advisor: Prof. Shlomo Zilberstein
  • Coursework: Artificial Intelligence, Reinforcement Learning, Robotics, Advanced Robot Dynamics & Control, Machine Learning, Neural Networks, Advanced Algorithms, Empirical Research Methods, Advanced Information Assurance
• M.S. in Computer Science, University of Massachusetts, Amherst, 2022. GPA 3.95/4.
• B.E. (Hons.) in Computer Science, Birla Institute of Technology and Science, Pilani, 2015. GPA 9.27/10.

Work experience

• Jun 2017 - Jul 2019: Research Engineer at School of Computing and Information Systems, Singapore Management University
  • Supervised by Prof. Pradeep Varakantham and Prof. Akshat Kumar
  • Worked on optimizing constrained resource allocation at city scale using deep reinforcement learning, which led to a publication.
• Aug 2015 - Jun 2017: Software Engineer at Walmart Labs, Bengaluru
  • Was part of Operations, Analytics & Research team for supply-chain division of Walmart’s eCommerce.
  • Developed an Elasticsearch based distributed database for data analysis.
  • Developed a deep-learning based system for anomaly-detection in large live incoming data streams.
• Jan 2015 - Jun 2015: Software Development Engineer Intern at Amazon, Bengaluru
  • Worked on offline experience for Prime Video.
  • Worked on optimizing content load time for Prime Video on Kindle tablets.

Programming Skills

• Languages: Experienced in Python, Julia, C/C++, Java. Familiar with C#, SQL.
• Frameworks: OpenAI Gym, PyTorch, FluxML, Tensorflow, CPLEX, Elasticsearch, Unity3D

See Github profile

Publications

RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$ NeurIPS GenPlan 2023

Bhatia, A., Nashed, SB., & Zilberstein, S. (2023). RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$. In NeurIPS Workshop on Generalization in Planning. URL PDF

Selecting the Partial State Abstractions of MDPs: A Metareasoning Approach with Deep Reinforcement Learning IROS 2022

Nashed, S.B., Svegliato, J., Bhatia, A., Russell S., Zilberstein, S. (2022). Selecting the partial state abstractions of MDPs: A metareasoning approach with deep reinforcement learning. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. PDF

Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning ICAPS 2022

Bhatia, A., Svegliato, J., Nashed, S. B., & Zilberstein, S. (2022). Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning. In Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 556-564. URL PDF

Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL ArXiv 2022

Bhatia, A., Thomas, PS., & Zilberstein, S. (2022). Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL. In arXiv preprint arXiv:2206.02380. URL PDF

On the Benefits of Randomly Adjusting Anytime Weighted A* SoCS 2021

Bhatia, A., Svegliato, J., & Zilberstein, S. (2021). On the Benefits of Randomly Adjusting Anytime Weighted A. In Proceedings of the International Symposium on Combinatorial Search (Vol. 12, No. 1, pp. 116-120). URL PDF

Tuning the Hyperparameters of Anytime Planning: A Deep Reinforcement Learning Approach ICAPS HSDIP 2021

Bhatia, A., Svegliato, J., & Zilberstein, S. (2021). Tuning the Hyperparameters of Anytime Planning: A Deep Reinforcement Learning Approach. In ICAPS Workshop on Heuristics and Search for Domain-independent Planning. URL PDF

Resource Constrained Deep Reinforcement Learning ICAPS 2019

Bhatia, A., Varakantham, P., & Kumar, A. (2019). Resource Constrained Deep Reinforcement Learning. In Proceedings of the International Conference on Automated Planning and Scheduling, 29(1), 610-620. URL PDF

Teaching

Teaching Assistant | CS383 Artificial Intelligence Fall 2022

College of Information & Computer Sciences, University of Massachusetts Amherst
Responsible for designing quizzes, clarifying students’ doubts and holding office hours.

Service Summary

• Program Committee member, IJCAI 2024.
• Program Committee member, NeurIPS 2023 GenPlan workshop.
• Paper reviewer, JMLR, 2023.
• Program Committee member, AAAI 2023.
• Paper reviewer, AIJ, 2021.
• As a member of IEEE BITS-Pilani chapter, conceptualized, developed and organized an AI bot making competition for a video game at college tech festival 2014.

Projects

Undergrad Projects