33 citations · 80 across the 7 of their papers we have counts for
8 papers
Reading the Room: Foundations, Design, and Challenges of Normative Competence in LLMs
Andrea Wynn, Harsh Satija, Nathan Baek +5
Human communities are governed by normative systems: shared standards that produce \textit{norms} dictating acceptable behavior, enforced through community sanctioning. Aligning in…
Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate
Andrea Wynn, Harsh Satija, Gillian Hadfield
While multi-agent debate has been proposed as a promising strategy for improving AI reasoning ability, we find that debate can sometimes be harmful rather than helpful. Prior work…
A Survey of Exploration Methods in Reinforcement Learning
Susan Amin, Maziar Gomrokchi, Harsh Satija +2
Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments. Reinfor…
Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs
Harsh Satija, Philip S. Thomas, Joelle Pineau +1
We study the problem of Safe Policy Improvement (SPI) under constraints in the offline Reinforcement Learning (RL) setting. We consider the scenario where: (i) we have a dataset co…
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi +2
A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action space…
Constrained Markov Decision Processes via Backward Value Functions
Harsh Satija, Philip Amortila, Joelle Pineau
Although Reinforcement Learning (RL) algorithms have found tremendous success in simulated domains, they often cannot directly be applied to physical systems, especially in cases w…
Randomized Value Functions via Multiplicative Normalizing Flows
Ahmed Touati, Harsh Satija, Joshua Romoff +2
Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike t…
Decoupling Dynamics and Reward for Transfer Learning
Amy Zhang, Harsh Satija, Joelle Pineau
Current reinforcement learning (RL) methods can successfully learn single tasks but often generalize poorly to modest perturbations in task domain or training procedure. In this wo…