1.3k citations · 2.5k across the 29 of their papers we have counts for
6 papers · 1 filter
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…
Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift
Riashat Islam, Komal K. Teru, Deepak Sharma +1
Off-policy deep reinforcement learning (RL) algorithms are incapable of learning solely from batch offline data without online interactions with the environment, due to the phenome…
Transfer Learning by Modeling a Distribution over Policies
Disha Shrivastava, Eeshan Gunesh Dhekane, Riashat Islam
Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution…
InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse +5
A central challenge in reinforcement learning is discovering effective policies for tasks where rewards are sparsely distributed. We postulate that in the absence of useful reward…