Reinforcement Learning

blogs

Extracting cues of causality from Medical text to mine factors related to Sjögren’s Syndrome

-- Dr. Sunandan Chakraborty -- The web is a sea of information where the much-needed information may just seem a click away! However, mining exhaustive information from the web is not a trivial and straightforward task as it looks to be. Therefore, AI researchers are developing natural language processing models to mine relevant information from the web. One such researcher is Dr. Sunandan Chakraborty, Assistant Professor at the School of Informatics and Computing at Indiana University.

• Aug 23, 2022

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Improving Sample Efficiency in Evolutionary RL using Off-policy Ranking

--Dr. Gugan Thoppe-- Reinforcement learning is a type of machine learning technique where the agent learns from the interactive environment by trial and error and has applications in sectors such as self-driving cars, marketing, robotics, gaming, manufacturing etc. However, at times it is difficult to apply reinforcement learning in situations where objective function features such as linearity, convexity or differentiability often are non-existent or difficult to detect. This is where Evolutionary strategy RL can help.

• Jun 6, 2022

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CombSGPO: A new algorithm to protect wildlife

Poaching and illegal smuggling of wildlife have remained a cause of concern for wildlife authorities. As per the World Wide Fund for Nature (WWF), Wildlife trade poses the second-biggest direct threat to the survival of species after habitat destruction. As per the study conducted by TRAFFIC, a leading wildlife trade monitoring networking of the World Wide Fund for Nature (WWF), around 1,11,312 individual tortoises or freshwater turtles (11,000 a year) have been illegally traded across India since 2009.

By Aravind Venugopal , By Elizabeth Bondi

• Sep 17, 2021

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Finding Influencers in Social Networks: Reinforcement Learning Shows the Way

Social Network Analysis has given us many tools to effectively manage information dissemination in a social group, study growth and dynamics in such groups, etc. But one of the key challenges when studying social groups of underprivileged or socially marginalized groups is the recovery of the underlying social network itself. This study proposes a machine learning approach for learning to effectively allocate a limited budget to discover the network.

By B Ravindran , By Harshavardhan P V

• Sep 5, 2020

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events

RBCDSAI Seminar by Prof. Gugan Thoppe

May 2, 2023 04:00 PM to 05:00 PM

BT Seminar Hall

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RBCDSAI Distinguished Fellow Colloquium by Prof. Sriram Natarajan

April 25, 2023 to April 27, 2023 09:00 AM to 12:00 PM

IC&SR Hall 2

artificial intelligence , deep learning , reinforcement-learning

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IBSE ARP Talk - Prof. Vijay Tiwari

April 5, 2023 04:30 PM to 05:30 PM

IC&SR Hall 2

artificial intelligence , deep learning , reinforcement-learning

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Eighth RBCDSAI LatentView Colloquium by Dr. Rema Padman

March 16, 2023 05:00 PM to 06:30 PM

IC&SR Hall 2

LatentView-Colloquium , artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar Series - Prof Lavanya Marla

January 3, 2023 10:30 AM to 11:30 AM

Seminar Hall, RBCDSAI

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Prof Madhav Marathe

December 15, 2022 04:00 PM to 05:00 PM

CRC301, IITM

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Gugan Thoppe

October 7, 2022 04:15 PM to 05:15 PM

Seminar Hall, Block I, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Dr. Vivek Kumar

July 11, 2022 05:00 PM to 06:00 PM

Aryabhatta Hall, CSB 25, IITM

artificial intelligence , deep learning , reinforcement-learning

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Seventh RBCDSAI LatentView Colloquium by Prof. V. S. Subrahmanian

May 12, 2022 04:00 PM to 05:00 PM

Hybrid, CRC 302

LatentView-Colloquium , artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI Seminar - Gugan Thoppe

April 19, 2022 04:00 PM to 05:00 PM

CRC, IITM

artificial intelligence , deep learning , reinforcement-learning

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RBCDSAI LatentView Colloquium Series

December 19, 2019 03:30 PM to 05:30 PM

ICSR Auditorium , IIT Madras

LatentView Colloquium , reinforcement learning

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people

Balaraman Ravindran

Head, Robert Bosch Centre for Data Science and AI, Professor, Mindtree Faculty Fellow

Specialization:

Machine learning, Reinforcement learning, Social Network Analysis, Data and text mining

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Chandra Shekar Lakshminarayanan

Assistant Professor

Specialization:

Reinforcement learning, Stochastic control and Deep Learning

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Gugan Thoppe

Assistant Professor, IISc

Specialization:

Stochastic Approximation, Random Topology, Reinforcement Learning

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Varun Balaji

Project Associate

Specialization:

Reinforcement Learning

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Yelchuri Venkata Sai Harsha

Post Baccalaureate Fellow

Specialization:

Robotics, Reinforcement Learning, Artificial Intelligence

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Vishnu Vinod

Post Baccalaureate Fellow

Specialization:

Reinforcement Learning, Differential Privacy, Machine Learning Theory

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Bavish Kulur

Post Baccalaureate Fellow

Specialization:

Reinforcement Learning, Graph Machine Learning

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Adhil Ahmed P M Shums

Post Baccalaureate Fellow

Specialization:

Big data, data visualisation, NLP, financial forensics, reinforcement learning, deep learning

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Shishir Avinash Nanoty

MS Scholar

Specialization:

Multi-armed bandits, Reinforcement Learning, Operations Research

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Siddarth Chandrasekar

Post Baccalaureate Fellow

Specialization:

Reinforcement Learning

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Devika Jay

Post Doctoral Fellow

Specialization:

Process optimisation, Electricity Markets, Reinforcement Learning

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Kartik Bharadwaj

MS Scholar

Specialization:

Reinforcement Learning, Robotics, Medicine

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Argha Boksi

MS Scholar

Specialization:

Machine Learning, Deep Learning, Reinforcement Learning

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Abhor Gupta

Post Baccalaureate Fellow

Specialization:

Reinforcement Learning

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Ashutosh Dilipbhai Kakadiya

MS Scholar

Specialization:

Reinforcement Learning, Relational Learning, Applied ML

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Dhruv Ramani

Project Associate

Specialization:

Reinforcement Learning, Deep Learning

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Debargha Bhattacharjee

MS Scholar

Specialization:

Natural Language Processing, Reinforcement Learning

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Aravind Venugopal

Post Baccalaureate Fellow

Specialization:

Reinforcement Learning, Game theory

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Returaj Burnwal

PhD Scholar

Specialization:

Reinforcement Learning, Mathematical Modeling

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Nishant Prabhu

M. Tech

Specialization:

Reinforcement Learning, Self-supervised representation learning for vision, Explainable AI

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Pavan Ravishankar

MS Scholar, Project Associate

Specialization:

Causal Inference, Reinforcement Learning

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Jahnvi Patel

Dual Degree

Specialization:

Reinforcement Learning

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Harshavardhan P V

Dual Degree

Specialization:

Reinforcement learning, Network reprsentation learning

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Chandrasekar Subramanian

PhD Scholar

Specialization:

Reinforcement learning

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Anand A R

Dual degree

Specialization:

Reinforcement Learning, Applied deep learning, Comutational biology

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Sivapriya Vellaichamy

Post Baccalaureate Fellow

Specialization:

Deep Learning, Reinforcement Learning

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Raghavendra Mallela

Project Associate

Specialization:

Reinforcement Learning, Stochastic Modeling

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Manoj Bharadhwaj

MS Scholar

Specialization:

Deep Learning, Reinforcement Learning, Intelligent Transportation Systems, Explainable AI

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Siddharth Nishtala

MS Scholar

Specialization:

Deep Learning, Reinforcement Learning

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projects

Nirav Bhatt , B. Ravindran

Human-in-the-loop for Safe and Verifiable Reinforcement Learning

AI Safety Human in the Loop

Mitesh M Khapra

Attention Mechanisms in Deep Neural Networks

deep learning reinforcement learning

Nandan Sudarsanam

Learning With Limited Partial and Noisy Data

reinforcement learning applied statistics

publications

PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

Saket Gurukar, Shaileshh Bojja Venkatakrishnan, Balaraman Ravindran, Srinivasan Parthasarathy,

To be appearing in the proceedings of ASONAM 2023

https://doi.org/10.48550/arXiv.2306.14357

End-to-end Autonomous Driving in Heterogeneous Traffic Scenario Using Deep Reinforcement Learning

Soumyajit Chakraborty, Subhadeep Kumar, Nirav Bhatt, Ramkrishna Pasumarthy,

European Control Conference (ECC)

Optimizing Traffic Control with Model-Based Learning: A Pessimistic Approach to Data-Efficient Policy Inference

Mayuresh Kunir, Sanjay Chawla, Siddarth Chandrasekar, Devika Jay, Balaraman Ravindran,

To appear in the Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023)

Physics-Informed Model-Based Reinforcement Learning

Adithya Ramesh, Balaraman Ravindran,

To appear in Learning for Dynamics & Control Conference (L4DC) 2023

RePReL: A Unified Framework for Integrating Relational Planning and Reinforcement Learning for Effective Abstraction in Discrete and Continuous Domains

Harsha Kokel, Sriraam Natarajan, B Ravindran, Prasad Tadepalli,

Neural Computing and Applications. Springer

https://doi.org/10.1007/s00521-022-08119-y

An Active Learning Framework for Efficient Robust Policy Search

Sai Kiran Narayanaswami, Nandan Sudarsanam, B Ravindran,

5th Joint International Conference on Data Science & Management of Data (CODS-COMAD 2022)

https://dl.acm.org/doi/10.1145/3493700.3493712

Smooth Imitation Learning via Smooth Costs and Smooth Policies

Sapana Chauhary, B Ravindran,

5th Joint International Conference on Data Science & Management of Data (CODS-COMAD 2022)

https://dl.acm.org/doi/abs/10.1145/3493700.3493716

Evolutionary Approach to Security Games with Signaling

Adam Zychowski, Jacek Mandziuk, Elizabeth Bondi, Aravind Venugopal, Milind Tambe, Balaraman Ravindran,

2022 International Joint Conference on Artificial Intelligence

[https://arxiv.org/abs/2204.14173]

Causal Contextual Bandits with Targeted Interventions

Chandrasekar Subramanian, Balaraman Ravindran,

The Tenth International Conference on Learning Representations - ICLR 2022

https://openreview.net/pdf?id=F5Em8ASCosV

Scalable multi-product inventory control with lead time constraints using reinforcement learning

Hardik Meisheri, Nazneen N. Sultana, Mayank Baranwal, Vinita Baniwal, Somjit Nath, Satyam Verma, Balaraman Ravindran, Harshad Khadilkar ,

Neural Computing and Applications

https://doi.org/10.1007/s00521-021-06129-w

RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction

Harsha Kokel, Arjun Manoharan, Sriraam Natarajan, Balaraman Ravindran, Prasad Tadepalli,

Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 31, pp. 533-541)

https://ojs.aaai.org/index.php/ICAPS/article/view/16001

Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertainty

Aravind Venugopal, Elizabeth Bondi, Harshavardhan Kamarthi, Keval Dholakia, B Ravindran, Milind Tambe,

In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1353–1361)

https://dl.acm.org/doi/10.5555/3463952.3464108

SEERL: Sample Efficient Ensemble Reinforcement Learning

Rohan Saphal, B Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, Bharat Kaul,

Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1100–1108)

https://dl.acm.org/doi/abs/10.5555/3463952.3464080

An Enhanced Advising Model in Teacher-Student framework using State Categorization

Daksh Anand, Vaibhav Gupta, Praveen Paruchuri, B Ravindran,

Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6653-6660

https://ojs.aaai.org/index.php/AAAI/article/view/16823

Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains

Nazneen N Sultana, Hardik Meisheri, Vinita Baniwal, Somjit Nath, Balaraman Ravindran, Harshad Khadilkar,

arXiv:2006.04037

https://arxiv.org/pdf/2006.04037.pdf

Dynamic Action Repetition for Deep Reinforcement Learning

Aravind S Lakshminarayanan, Sahil Sharma, B Ravindran,

Proceedings of the AAAI Conference on Artificial Intelligence

https://dl.acm.org/doi/10.5555/3298483.3298547

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, B Ravindran,

ICLR

https://arxiv.org/abs/1610.01283v4

Dynamic frame skip deep q network

Aravind S Lakshminarayanan, Sahil Sharma, B Ravindran,

IJCAI 2016 Workshop on Deep Reinforcement Learning: Frontiers and Challenges

https://arxiv.org/abs/1605.05365

An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning

Pragathi P Balasubramani, V Srinivasa Chakravarthy, B Ravindran, Ahmed A Moustafa,

Frontiers in computational neuroscience

https://doi.org/10.3389/fncom.2014.00047

research

Safety and Stability Preserving Reinforcement Learning

reinforcement learning deep learning

Attention Mechanisms in Deep Neural Networks

neural networks reinforcement learning