9 papers
A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations
Sohan Rudra, Saksham Goel, Anirban Santara +6
Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most…
Unlocking Pixels for Reinforcement Learning via Implicit Attention
Krzysztof Marcin Choromanski, Deepali Jain, Wenhao Yu +9
There has recently been significant interest in training reinforcement learning (RL) agents in vision-based environments. This poses many challenges, such as high dimensionality an…
MADRaS : Multi Agent Driving Simulator
Anirban Santara, Sohan Rudra, Sree Aditya Buridi +4
In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS prov…
ExTra: Transfer-guided Exploration
Anirban Santara, Rishabh Madan, Balaraman Ravindran +1
In this work we present a novel approach for transfer-guided exploration in reinforcement learning that is inspired by the human tendency to leverage experiences from similar encou…
PUNCH: Positive UNlabelled Classification based information retrieval in Hyperspectral images
Anirban Santara, Jayeeta Datta, Sourav Sarkar +3
Hyperspectral images of land-cover captured by airborne or satellite-mounted sensors provide a rich source of information about the chemical composition of the materials present in…
RAIL: Risk-Averse Imitation Learning
Anirban Santara, Abhishek Naik, Balaraman Ravindran +4
Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a s…