1 citations · 3 across the 7 of their papers we have counts for
7 papers
Extending Differential Temporal Difference Methods for Episodic Problems
Kris De Asis, Mohamed Elsayed, Jiamin He
Differential temporal difference (TD) methods are value-based reinforcement learning algorithms that have been proposed for infinite-horizon problems. They rely on reward centering…
Intentional Updates for Streaming Reinforcement Learning
Arsalan Sharifnassab, Mohamed Elsayed, Kris De Asis +2
In gradient-based learning, a step size chosen in parameter units does not produce a predictable per-step change in function output. This often leads to instability in the streamin…
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers
Gautham Vasan, Mohamed Elsayed, Alireza Azimi +5
Modern deep policy gradient methods achieve effective performance on simulated robotic tasks, but they all require large replay buffers or expensive batch updates, or both, making…
Streaming Deep Reinforcement Learning Finally Works
Mohamed Elsayed, Gautham Vasan, Elena Sorina Lupu +1
Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streaming learning has remained a persis…
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning
Mohamed Elsayed, Homayoon Farrahi, Felix Dangel +1
Second-order information is valuable for many applications but challenging to compute. Several works focus on computing or approximating Hessian diagonals, but even this simplifica…
Weight Clipping for Deep Continual and Reinforcement Learning
Mohamed Elsayed, Qingfeng Lan, Clare Lyle +1
Many failures in deep continual and reinforcement learning are associated with increasing magnitudes of the weights, making them hard to change and potentially causing overfitting.…
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning
Mohamed Elsayed, A. Rupam Mahmood
Deep representation learning methods struggle with continual learning, suffering from both catastrophic forgetting of useful units and loss of plasticity, often due to rigid and un…