213 citations · 954 across the 24 of their papers we have counts for
6 papers · 1 filter
Learning in Factored Domains with Information-Constrained Visual Representations
Tailia Malloy, Miao Liu, Matthew D. Riemer +3
Humans learn quickly even in tasks that contain complex visual information. This is due in part to the efficient formation of compressed representations of visual information, allo…
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games
Tailia Malloy, Tim Klinger, Miao Liu +3
This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorit…
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi +6
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential deci…
Efficient Black-Box Planning Using Macro-Actions with Focused Effects
Cameron Allen, Michael Katz, Tim Klinger +3
The difficulty of deterministic planning increases exponentially with search-tree depth. Black-box planning presents an even greater challenge, since planners must operate without…
The Eigenoption-Critic Framework
Miao Liu, Marlos C. Machado, Gerald Tesauro +1
Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient explo…
Analysis of Watson's Strategies for Playing Jeopardy!
Gerald Tesauro, David C. Gondek, Jonathan Lenchner +2
Major advances in Question Answering technology were needed for IBM Watson to play Jeopardy! at championship level -- the show requires rapid-fire answers to challenging natural la…