46 citations · 120 across the 24 of their papers we have counts for
5 papers · 1 filter
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil +4
Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-ba…
Evaluating Defences against Unsafe Feedback in RLHF
Domenic Rosati, Giles Edkins, Harsh Raj +5
While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety guards can easily be removed whe…
Intelligent Switching for Reset-Free RL
Darshan Patil, Janarthanan Rajendran, Glen Berseth +1
In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resetting} assumption limits the potential of…
Fairness Incentives in Response to Unfair Dynamic Pricing
Jesse Thibodeau, Hadi Nekoei, Afaf Taïk +2
The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' demand responses to a given pricing stra…
Mastering Memory Tasks with World Models
Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran +1
Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solve tasks involving extended time gaps bet…