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cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…