10 citations · 24 across the 9 of their papers we have counts for
3 papers · 1 filter
Policy-as-logic for robust reasoning over rules
Rahul Nair, Bastian Lipka, Elizabeth Daly
In many practical applications of generative AI systems, from tax rules to airline baggage allowance, responses to natural language queries must respect written policies or rules.…
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
Jasmina Gajcin, James McCarthy, Rahul Nair +3
A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging…
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati +3
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, b…