Pranav Agarwal

I am a Ph.D. candidate at Mila, advised by Sheldon Andrews and Samira Ebrahimi Kahou, working on reinforcement Learning for robotic applications and character animation. My research interests are in modeling efficient reinforcement learning algorithms utilizing large generative models. Towards this goal, I have worked on generative models (Transformers) for sample-efficient reinforcement learning and automating reward modeling (from large offline trajectories) for complex robotic applications like excavator automation.

Previously, I was a student researcher at Inria where I collaborated with Natalia Díaz-Rodríguez and Raoul de CHARETTE. I completed my Bachelors in Electronics and Communication Engineering at IIIT Guwahati, where I was awarded the President's Gold Medal. During my bachelor's I worked as a research intern at SUTD with Professor Gemma Roig.

I'm looking for research positions and open to collaborations. Feel free to reach out!

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This survey explores the impact of transformers in reinforcement learning, addressing common RL challenges, while examining their applications in representation learning, policy optimization, and interpretability.

This work introduces Discrete Abstract Representations for Transformer-based Learning (DART), a sample-efficient method that utilizes discrete representations to improve world modeling and learning behavior in reinforcement learning, achieving superior performance on the Atari 100k benchmark compared to existing methods.

The Medical Decision Transformer (MeDT) leverages the transformer architecture to enhance offline reinforcement learning for sepsis treatment recommendations, utilizing a goal-conditioned RL paradigm that improves interpretability and clinician interactivity, while achieving competitive results on the MIMIC-III dataset.

This paper introduces TPTO, a Deep Reinforcement Learning approach that utilizes Transformer and Proximal Policy Optimization to efficiently offload dependent IoT tasks to edge servers, significantly reducing latency for IoT applications compared to state-of-the-art methods.

A novel automatic evaluation strategy for excavator operators is proposed, utilizing machine dynamics and safety criteria, which is then validated through reinforcement learning in a simulation, resulting in safer and more realistic excavator maneuvering policies.

A curriculum-based deep reinforcement learning approach for end-to-end driving is proposed, using sparse rewards and navigation view maps to achieve generalization on unseen roads and longer distances.

A new image captioning dataset, Egoshots, is introduced alongside a novel evaluation metric, Semantic Fidelity, to address biases in existing models and enable caption assessment without annotations.

A framework for cross-modal biometric matching is proposed, generating faces from voice clips using various generative networks, with RC-GAN achieving the best identity accuracy of 84.52% and VAE producing the highest quality images.