4 papers · 1 filter
Trace-Mediated Peak Bias: Bridging Temporal Credit Assignment and Cognitive Heuristics in Deep Reinforcement Learning
Viktor Veselý, Aleksandar Todorov, Erwan Escudie +1
Temporal credit assignment is central to both biological and artificial intelligence, yet its interaction with non-linear function approximation is poorly understood. We identify a…
Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning
Aleksandar Todorov, Matthia Sabatelli
Deep reinforcement learning (RL) agents commonly rely on high-dimensional neural representations, despite growing evidence that task-relevant value and policy structure may be intr…
On The Presence of Double-Descent in Deep Reinforcement Learning
Viktor Veselý, Aleksandar Todorov, Matthia Sabatelli
The double descent (DD) paradox, where over-parameterized models see generalization improve past the interpolation point, remains largely unexplored in the non-stationary domain of…
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning
Aleksandar Todorov, Juan Cardenas-Cartagena, Rafael F. Cunha +2
Plasticity loss, a diminishing capacity to adapt as training progresses, is a critical challenge in deep reinforcement learning. We examine this issue in multi-task reinforcement l…