103 citations · 355 across the 24 of their papers we have counts for
15 papers · 1 filter
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
Hadi Nekoei, Xutong Zhao, Janarthanan Rajendran +2
Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in recent years. ZSC refers to the ability of a…
Influencing Long-Term Behavior in Multiagent Reinforcement Learning
Dong-Ki Kim, Matthew Riemer, Miao Liu +5
The main challenge of multiagent reinforcement learning is the difficulty of learning useful policies in the presence of other simultaneously learning agents whose changing behavio…
Local Explanations for Reinforcement Learning
Ronny Luss, Amit Dhurandhar, Miao Liu
Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood b…
Learning Multi-Objective Curricula for Robotic Policy Learning
Jikun Kang, Miao Liu, Abhinav Gupta +3
Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL). They are designe…
Context-Specific Representation Abstraction for Deep Option Learning
Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer +3
Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.…
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…
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…
On the Role of Weight Sharing During Deep Option Learning
Matthew Riemer, Ignacio Cases, Clemens Rosenbaum +2
The options framework is a popular approach for building temporally extended actions in reinforcement learning. In particular, the option-critic architecture provides general purpo…
Learning Hierarchical Teaching Policies for Cooperative Agents
Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei +7
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers. In particular, recent work studying agents that learn to teach other teamma…
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
Matthew Riemer, Ignacio Cases, Robert Ajemian +4
Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realist…
Learning Abstract Options
Matthew Riemer, Miao Liu, Gerald Tesauro
Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning. One popular approach for ad…
Eigenoption Discovery through the Deep Successor Representation
Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo +3
Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learn…
Deep Q-Networks for Accelerating the Training of Deep Neural Networks
Jie Fu
In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our ap…
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
Trevor Campbell, Miao Liu, Brian Kulis +2
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolvi…