4 citations · 10 across the 8 of their papers we have counts for
8 papers
NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning
Seyed Roozbeh Razavi Rohani, Khashayar Khajavi, Wesley Chung +2
Continual learning (CL) requires models to learn tasks sequentially, yet deep neural networks often suffer from plasticity loss and poor knowledge transfer, which can impede their…
Preserving Plasticity in Continual Learning with Adaptive Linearity Injection
Seyed Roozbeh Razavi Rohani, Khashayar Khajavi, Wesley Chung +2
Loss of plasticity in deep neural networks is the gradual reduction in a model's capacity to incrementally learn and has been identified as a key obstacle to learning in non-statio…
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung, Lynn Cherif, David Meger +1
Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks -- all referring to the incr…
The Role of Baselines in Policy Gradient Optimization
Jincheng Mei, Wesley Chung, Valentin Thomas +3
We study the effect of baselines in on-policy stochastic policy gradient optimization, and close the gap between the theory and practice of policy optimization methods. Our first c…
Beyond variance reduction: Understanding the true impact of baselines on policy optimization
Wesley Chung, Valentin Thomas, Marlos C. Machado +1
Bandit and reinforcement learning (RL) problems can often be framed as optimization problems where the goal is to maximize average performance while having access only to stochasti…
Incrementally Learning Functions of the Return
Brendan Bennett, Wesley Chung, Muhammad Zaheer +1
Temporal difference methods enable efficient estimation of value functions in reinforcement learning in an incremental fashion, and are of broader interest because they correspond…
Importance Resampling for Off-policy Prediction
Matthew Schlegel, Wesley Chung, Daniel Graves +2
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance u…
High-confidence error estimates for learned value functions
Touqir Sajed, Wesley Chung, Martha White
Estimating the value function for a fixed policy is a fundamental problem in reinforcement learning. Policy evaluation algorithms---to estimate value functions---continue to be dev…