360 citations · 2.7k across the 150 of their papers we have counts for
22 papers · 1 filter
Shaping representations through communication: community size effect in artificial learning systems
Olivier Tieleman, Angeliki Lazaridou, Shibl Mourad +2
Motivated by theories of language and communication that explain why communities with large numbers of speakers have, on average, simpler languages with more regularity, we cast th…
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…
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain, William Fedus, Hugo Larochelle +2
Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse…