5 citations · 9 across the 6 of their papers we have counts for
5 papers · 1 filter
Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
Manu Srinath Halvagal, Sebastian Lee, SueYeon Chung
Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience. Modern deep RL has shown remarkable success across many domains, further s…
Uncertainty Prioritized Experience Replay
Rodrigo Carrasco-Davis, Sebastian Lee, Claudia Clopath +1
Prioritized experience replay, which improves sample efficiency by selecting relevant transitions to update parameter estimates, is a crucial component of contemporary value-based…
A Theory of Initialisation's Impact on Specialisation
Devon Jarvis, Sebastian Lee, Clémentine Carla Juliette Dominé +2
Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of cata…
Lifelong Reinforcement Learning via Neuromodulation
Sebastian Lee, Samuel Liebana, Claudia Clopath +1
Navigating multiple tasks$\unicode{x2014}$for instance in succession as in continual or lifelong learning, or in distributions as in meta or multi-task learning$\unicode{x2014}$req…
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions
Nishil Patel, Sebastian Lee, Stefano Sarao Mannelli +2
Reinforcement learning (RL) algorithms have proven transformative in a range of domains. To tackle real-world domains, these systems often use neural networks to learn policies dir…