Portrait de Manoosh Samiei

Manoosh Samiei

manoosh.samiei@mila.quebec
Doctorat - McGill
Superviseur⋅e principal⋅e
Doina Precup
Co-supervisor
Paul Masset
Sujets de recherche
Apprentissage par renforcement
Neurosciences computationnelles
Raisonnement
Vision par ordinateur
Site web
Google Scholar
GitHub

Publications

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 le… (voir plus)arning (RL), this trade-off is typically controlled through a fixed discount factor, which imposes a single exponentially discounted temporal horizon. However, biological agents exhibit flexible and adaptive temporal discounting, suggesting that effective planning requires multiple timescales. Here, we propose a multi-horizon approach that adaptively selects and combines temporal horizons, enabling robust adaptation to changes in reward structure without manual discount-factor tuning. This flexibility makes the method particularly suitable for continual learning scenarios involving task switches and varying environmental configurations. Empirically, we demonstrate that our approach identifies effective discount factors across a range of MiniGrid environments, including continual settings composed of three sequentially changing tasks. These results suggest that adaptive temporal discounting can improve parameter efficiency and enhance adaptability in both artificial and biologically inspired learning systems.
2026-06-15
ContinualRL @ Reinforcement Learning Conference (publié)
doi.org
openreview.net