3 citations · 9 across the 15 of their papers we have counts for
4 papers · 1 filter
Ergodicity in reinforcement learning
Dominik Baumann, Erfaun Noorani, Arsenii Mustafin +5
In reinforcement learning, we typically aim to optimize the expected value of the sum of rewards an agent collects over a trajectory. However, if the process generating these rewar…
Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures
Erfaun Noorani, Pasan Dissanayake, Faisal Hamman +1
Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense int…
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
Rui Liu, Anish Gupta, Erfaun Noorani +1
Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their envir…
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations
Dominik Baumann, Erfaun Noorani, James Price +3
Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the re…