1 citations · 1 across the 5 of their papers we have counts for
5 papers
Match or Replay: Self Imitating Proximal Policy Optimization
Gaurav Chaudhary, Laxmidhar Behera, Washim Uddin Mondal
Reinforcement Learning (RL) agents often struggle with inefficient exploration, particularly in environments with sparse rewards. Traditional exploration strategies can lead to slo…
TEACH: Temporal Variance-Driven Curriculum for Reinforcement Learning
Gaurav Chaudhary, Laxmidhar Behera
Reinforcement Learning (RL) has achieved significant success in solving single-goal tasks. However, uniform goal selection often results in sample inefficiency in multi-goal settin…
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Gaurav Chaudhary, Laxmidhar Behera
Offline Reinforcement Learning (RL) aims to learn effective policies from a static dataset without requiring further agent-environment interactions. However, its practical adoption…
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Gaurav Chaudhary, Wassim Uddin Mondal, Laxmidhar Behera
Sample efficiency and exploration remain critical challenges in Deep Reinforcement Learning (DRL), particularly in complex domains. Offline RL, which enables agents to learn optima…
Bin-picking of novel objects through category-agnostic-segmentation: RGB matters
Prem Raj, Sachin Bhadang, Gaurav Chaudhary +2
This paper addresses category-agnostic instance segmentation for robotic manipulation, focusing on segmenting objects independent of their class to enable versatile applications li…