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20 papers · 2 filters
Marginalized State Distribution Entropy Regularization in Policy Optimization
Riashat Islam, Zafarali Ahmed, Doina Precup
Entropy regularization is used to get improved optimization performance in reinforcement learning tasks. A common form of regularization is to maximize policy entropy to avoid prem…
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
Riashat Islam, Raihan Seraj, Samin Yeasar Arnob +1
We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor-critic algorithms require an off-policy…
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods
Riashat Islam, Raihan Seraj, Pierre-Luc Bacon +1
The policy gradient theorem is defined based on an objective with respect to the initial distribution over states. In the discounted case, this results in policies that are optimal…
Hindsight Credit Assignment
Anna Harutyunyan, Will Dabney, Thomas Mesnard +8
We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit…
Meta-Graph: Few Shot Link Prediction via Meta Learning
Avishek Joey Bose, Ankit Jain, Piero Molino +1
We consider the task of few shot link prediction on graphs. The goal is to learn from a distribution over graphs so that a model is able to quickly infer missing edges in a new gra…
Towards Reducing Bias in Gender Classification
Komal K. Teru, Aishik Chakraborty
Societal bias towards certain communities is a big problem that affects a lot of machine learning systems. This work aims at addressing the racial bias present in many modern gende…