Search Results for author: Matthew Riemer

Found 26 papers, 8 papers with code

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria

no code implementations28 Oct 2022 Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Gerald Tesauro, Jonathan P. How

By directly comparing active equilibria to Nash equilibria in these examples, we find that active equilibria find more effective solutions than Nash equilibria, concluding that an active equilibrium is the desired solution for multiagent learning settings.

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

1 code implementation7 Mar 2022 Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How

An effective approach that has recently emerged for addressing this non-stationarity is for each agent to anticipate the learning of other agents and influence the evolution of future policies towards desirable behavior for its own benefit.

reinforcement-learning Reinforcement Learning (RL)

Context-Specific Representation Abstraction for Deep Option Learning

1 code implementation20 Sep 2021 Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How

Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.

Hierarchical Reinforcement Learning

Towards Continual Reinforcement Learning: A Review and Perspectives

no code implementations25 Dec 2020 Khimya Khetarpal, Matthew Riemer, Irina Rish, Doina Precup

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 RL.

Continual Learning reinforcement-learning +1

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

no code implementations23 Nov 2020 Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims

This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm.

Continual Learning Continuous Control +2

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

1 code implementation31 Oct 2020 Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How

A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.

reinforcement-learning Reinforcement Learning (RL)

Deep RL With Information Constrained Policies: Generalization in Continuous Control

no code implementations9 Oct 2020 Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro

We focus on the model-free reinforcement learning (RL) setting and formalize our approach in terms of an information-theoretic constraint on the complexity of learned policies.

Continuous Control reinforcement-learning +1

A Study of Compositional Generalization in Neural Models

no code implementations16 Jun 2020 Tim Klinger, Dhaval Adjodah, Vincent Marois, Josh Joseph, Matthew Riemer, Alex 'Sandy' Pentland, Murray Campbell

One difficulty in the development of such models is the lack of benchmarks with clear compositional and relational task structure on which to systematically evaluate them.

Image Classification Relational Reasoning

Efficient Black-Box Planning Using Macro-Actions with Focused Effects

2 code implementations28 Apr 2020 Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro

Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.

Coagent Networks Revisited

1 code implementation28 Jan 2020 Modjtaba Shokrian Zini, Mohammad Pedramfar, Matthew Riemer, Ahmadreza Moradipari, Miao Liu

Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment.

Hierarchical Reinforcement Learning reinforcement-learning

On the Role of Weight Sharing During Deep Option Learning

no code implementations31 Dec 2019 Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro

In this work we note that while this key assumption of the policy gradient theorems of option-critic holds in the tabular case, it is always violated in practice for the deep function approximation setting.

Atari Games Test

Hierarchical Average Reward Policy Gradient Algorithms

no code implementations20 Nov 2019 Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar

Option-critic learning is a general-purpose reinforcement learning (RL) framework that aims to address the issue of long term credit assignment by leveraging temporal abstractions.

Reinforcement Learning (RL)


no code implementations25 Sep 2019 Tyler James Malloy, Matthew Riemer, Miao Liu, Tim Klinger, Gerald Tesauro, Chris R. Sims

We formalize this type of bounded rationality in terms of an information-theoretic constraint on the complexity of policies that agents seek to learn.

Continuous Control reinforcement-learning +1

Continual Learning with Self-Organizing Maps

no code implementations19 Apr 2019 Pouya Bashivan, Martin Schrimpf, Robert Ajemian, Irina Rish, Matthew Riemer, Yuhai Tu

Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones.

Continual Learning

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

2 code implementations ICLR 2019 Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro

In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples.

Continual Learning Meta-Learning

Learning Abstract Options

no code implementations NeurIPS 2018 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.

PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences

no code implementations17 Oct 2018 Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic

Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences.

Learning to Teach in Cooperative Multiagent Reinforcement Learning

no code implementations20 May 2018 Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How

The problem of teaching to improve agent learning has been investigated by prior works, but these approaches make assumptions that prevent application of teaching to general multiagent problems, or require domain expertise for problems they can apply to.

reinforcement-learning Reinforcement Learning (RL)

Scalable Recollections for Continual Lifelong Learning

no code implementations17 Nov 2017 Matthew Riemer, Tim Klinger, Djallel Bouneffouf, Michele Franceschini

Given the recent success of Deep Learning applied to a variety of single tasks, it is natural to consider more human-realistic settings.

Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning

no code implementations12 Apr 2017 Matthew Riemer, Elham Khabiri, Richard Goodwin

We demonstrate our approach on a Twitter domain sentiment analysis task with sequential knowledge transfer from four related tasks.

Sentiment Analysis Transfer Learning

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