213 citations · 954 across the 24 of their papers we have counts for
5 papers · 1 filter
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…
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
Dong-Ki Kim, Miao Liu, Matthew Riemer +6
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents. In particular, each…
Deep RL With Information Constrained Policies: Generalization in Continuous Control
Tailia Malloy, Chris R. Sims, Tim Klinger +3
Biological agents learn and act intelligently in spite of a highly limited capacity to process and store information. Many real-world problems involve continuous control, which rep…
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…