3 citations · 6 across the 14 of their papers we have counts for
4 papers · 1 filter
Offline Constrained RLHF with Multiple Preference Oracles
Brenden Latham, Mehrdad Moharrami
We study offline constrained reinforcement learning from human feedback with multiple preference oracles. Motivated by applications that trade off performance with safety or fairne…
Tail Distribution of Regret in Optimistic Reinforcement Learning
Sajad Khodadadian, Mehrdad Moharrami
We derive instance-dependent tail bounds for the regret of optimism-based reinforcement learning in finite-horizon tabular Markov decision processes with unknown transition dynamic…
On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes
Yashaswini Murthy, Mehrdad Moharrami, R. Srikant
Modified policy iteration (MPI) is a dynamic programming algorithm that combines elements of policy iteration and value iteration. The convergence of MPI has been well studied in t…
Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms
Yashaswini Murthy, Mehrdad Moharrami, R. Srikant
Many policy-based reinforcement learning (RL) algorithms can be viewed as instantiations of approximate policy iteration (PI), i.e., where policy improvement and policy evaluation…