41 citations · 134 across the 23 of their papers we have counts for
4 papers · 1 filter
Dynamic probabilistic logic models for effective abstractions in RL
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan +2
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hie…
Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1
Jahnvi Patel, Devika Jay, Balaraman Ravindran +1
In real time electricity markets, the objective of generation companies while bidding is to maximize their profit. The strategies for learning optimal bidding have been formulated…
A Causal Linear Model to Quantify Edge Flow and Edge Unfairness for UnfairEdge Prioritization and Discrimination Removal
Pavan Ravishankar, Pranshu Malviya, Balaraman Ravindran
Law enforcement must prioritize sources of unfairness before mitigating their underlying unfairness, considering that they have limited resources. Unlike previous works that only m…
Reinforcement Learning for Multi-Objective Optimization of Online Decisions in High-Dimensional Systems
Hardik Meisheri, Vinita Baniwal, Nazneen N Sultana +2
This paper describes a purely data-driven solution to a class of sequential decision-making problems with a large number of concurrent online decisions, with applications to comput…