12 citations · 67 across the 68 of their papers we have counts for
11 papers · 2 filters
From Connectivity to Rewards: Dense Reward Learning with Directed State Graphs
Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang +1
The integration of graphs with Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) has received increasing attention, as graphs naturally encode task hierarchies for effec…
A Bellman Optimality Equation for Plasticity
Jeremy Lucas, Doina Precup
In continual reinforcement learning, carefully managing the stability-plasticity tradeoff remains a core challenge. Recent work by Abel et al. (2025) formalized this dilemma by def…
Analytic Planning under Uncertainty with Moment Closure
Shishir Sharma, Doina Precup
Effective model-based reinforcement learning in stochastic environments requires planning that accounts for predictive uncertainty. Propagating full state distributions analyticall…
Adaptive Multi-Horizon Reinforcement Learning
Manoosh Samiei, Doina Precup, Paul Masset
Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences. In reinforcement learning (RL), this trade-off is typically…
Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning
Anthony GX-Chen, Ankit Anand, Gheorghe Comanici +7
Classical reinforcement learning (RL) typically seeks a deterministic policy that maximizes the expected sum of a scalar reward. Yet, modern applications such as language model fin…
Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems
Jonathan Colaço Carr, Prakash Panangaden, Doina Precup +1
Reinforcement learning with scalar rewards is widely used for aligning machine-learning systems with user preferences. But, pairwise preferences are often more natural for users to…