9 citations · 18 across the 5 of their papers we have counts for
9 papers
An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment
Sina Ghiassian, Richard S. Sutton
Many off-policy prediction learning algorithms have been proposed in the past decade, but it remains unclear which algorithms learn faster than others. We empirically compare 11 of…
An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task
Sina Ghiassian, Richard S. Sutton
Off-policy prediction -- learning the value function for one policy from data generated while following another policy -- is one of the most challenging subproblems in reinforcemen…
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?
Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton
Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despit…
Gradient Temporal-Difference Learning with Regularized Corrections
Sina Ghiassian, Andrew Patterson, Shivam Garg +3
It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems…
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks
Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo +1
Reinforcement learning systems require good representations to work well. For decades practical success in reinforcement learning was limited to small domains. Deep reinforcement l…
Should All Temporal Difference Learning Use Emphasis?
Xiang Gu, Sina Ghiassian, Richard S. Sutton
Emphatic Temporal Difference (ETD) learning has recently been proposed as a convergent off-policy learning method. ETD was proposed mainly to address convergence issues of conventi…