22 citations · 58 across the 12 of their papers we have counts for
12 papers
Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing
Bhiman Kumar Baghel, Emma Jordan, Zheyuan Ryan Shi +1
Large Language Models (LLMs) are widely deployed in downstream tasks, but keeping their knowledge up-to-date via retraining or fine-tuning is often computationally expensive. Model…
Position: Benchmarking is Limited in Reinforcement Learning Research
Scott M. Jordan, Adam White, Bruno Castro da Silva +2
Novel reinforcement learning algorithms, or improvements on existing ones, are commonly justified by evaluating their performance on benchmark environments and are compared to an e…
A New View on Planning in Online Reinforcement Learning
Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan +2
This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, sim…
From Past to Future: Rethinking Eligibility Traces
Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari +3
In this paper, we introduce a fresh perspective on the challenges of credit assignment and policy evaluation. First, we delve into the nuances of eligibility traces and explore ins…
Behavior Alignment via Reward Function Optimization
Dhawal Gupta, Yash Chandak, Scott M. Jordan +2
Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task. This is challenging since it requires the identif…
Coagent Networks: Generalized and Scaled
James E. Kostas, Scott M. Jordan, Yash Chandak +5
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic…
Goal-Space Planning with Subgoal Models
Chunlok Lo, Kevin Roice, Parham Mohammad Panahi +5
This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, sim…
Towards Safe Policy Improvement for Non-Stationary MDPs
Yash Chandak, Scott M. Jordan, Georgios Theocharous +2
Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that a…
Evaluating the Performance of Reinforcement Learning Algorithms
Scott M. Jordan, Yash Chandak, Daniel Cohen +2
Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning. Recent reproducibility analyses have shown that reported performance results ar…
Classical Policy Gradient: Preserving Bellman's Principle of Optimality
Philip S. Thomas, Scott M. Jordan, Yash Chandak +2
We propose a new objective function for finite-horizon episodic Markov decision processes that better captures Bellman's principle of optimality, and provide an expression for the…
Learning Action Representations for Reinforcement Learning
Yash Chandak, Georgios Theocharous, James Kostas +2
Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the str…
Distributed Evaluations: Ending Neural Point Metrics
Daniel Cohen, Scott M. Jordan, W. Bruce Croft
With the rise of neural models across the field of information retrieval, numerous publications have incrementally pushed the envelope of performance for a multitude of IR tasks. H…