35 citations · 89 across the 43 of their papers we have counts for
16 papers · 1 filter
Offline Safe Policy Optimization From Heterogeneous Feedback
Ze Gong, Pradeep Varakantham, Akshat Kumar
Offline Preference-based Reinforcement Learning (PbRL) learns rewards and policies aligned with human preferences without the need for extensive reward engineering and direct inter…
Preserving the Privacy of Reward Functions in MDPs through Deception
Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri
Preserving the privacy of preferences (or rewards) of a sequential decision-making agent when decisions are observable is crucial in many physical and cybersecurity domains. For in…
EduQate: Generating Adaptive Curricula through RMABs in Education Settings
Sidney Tio, Dexun Li, Pradeep Varakantham
There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in de…
Safety through feedback in Constrained RL
Shashank Reddy Chirra, Pradeep Varakantham, Praveen Paruchuri
In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe beh…
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators
Sidney Tio, Jimmy Ho, Pradeep Varakantham
We adapt Parameterized Environment Response Model (PERM), a method for training both Reinforcement Learning (RL) Agents and human learners in parameterized environments by directly…
Transferable Curricula through Difficulty Conditioned Generators
Sidney Tio, Pradeep Varakantham
Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial…