103 citations · 327 across the 23 of their papers we have counts for
7 papers · 1 filter
Towards Continual Reinforcement Learning: A Review and Perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish +1
In this article, we aim to provide a literature review of different formulations and approaches to continual reinforcement learning (RL), also known as lifelong or non-stationary R…
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…
A Study of Compositional Generalization in Neural Models
Tim Klinger, Dhaval Adjodah, Vincent Marois +4
Compositional and relational learning is a hallmark of human intelligence, but one which presents challenges for neural models. One difficulty in the development of such models is…
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…
Coagent Networks Revisited
Modjtaba Shokrian Zini, Mohammad Pedramfar, Matthew Riemer +2
Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment. Prominent examples of co…