b'Chris Amato'

research

∙ 07/22/2023

On-Robot Bayesian Reinforcement Learning for POMDPs

Robot learning is often difficult due to the expense of gathering data. ...

0 Hai Nguyen, et al. ∙

research

∙ 02/20/2023

Improving Deep Policy Gradients with Value Function Search

Deep Policy Gradient (PG) algorithms employ value networks to drive the ...

0 Enrico Marchesini, et al. ∙

research

∙ 02/20/2023

Safe Deep Reinforcement Learning by Verifying Task-Level Properties

Cost functions are commonly employed in Safe Deep Reinforcement Learning...

0 Enrico Marchesini, et al. ∙

research

∙ 01/26/2023

Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning

Off-policy learning from multistep returns is crucial for sample-efficie...

0 Brett Daley, et al. ∙

research

∙ 11/03/2022

Leveraging Fully Observable Policies for Learning under Partial Observability

Reinforcement learning in partially observable domains is challenging du...

0 Hai Nguyen, et al. ∙

research

∙ 09/20/2022

Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning

Synchronizing decisions across multiple agents in realistic settings is ...

0 Yuchen Xiao, et al. ∙

research

∙ 06/02/2022

Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

Real-world reinforcement learning tasks often involve some form of parti...

52 Kevin Esslinger, et al. ∙

research

∙ 04/02/2022

Hierarchical Reinforcement Learning under Mixed Observability

The framework of mixed observable Markov decision processes (MOMDP) mode...

0 Hai Nguyen, et al. ∙

research

∙ 02/17/2022

BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs

While reinforcement learning (RL) has made great advances in scalability...

0 Sammie Katt, et al. ∙

research

∙ 01/03/2022

A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning

Centralized Training for Decentralized Execution, where training is done...

0 Xueguang Lyu, et al. ∙

research

∙ 12/23/2021

Improving the Efficiency of Off-Policy Reinforcement Learning by Accounting for Past Decisions

Off-policy learning from multistep returns is crucial for sample-efficie...

0 Brett Daley, et al. ∙

research

∙ 12/06/2021

Virtual Replay Cache

Return caching is a recent strategy that enables efficient minibatch tra...

0 Brett Daley, et al. ∙

research

∙ 11/01/2021

Human-Level Control without Server-Grade Hardware

Deep Q-Network (DQN) marked a major milestone for reinforcement learning...

0 Brett Daley, et al. ∙

research

∙ 10/16/2021

Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning

Policy gradient methods have become popular in multi-agent reinforcement...

0 Yuchen Xiao, et al. ∙

research

∙ 06/10/2021

Investigating Alternatives to the Root Mean Square for Adaptive Gradient Methods

Adam is an adaptive gradient method that has experienced widespread adop...

0 Brett Daley, et al. ∙

research

∙ 06/07/2021

Reconciling Rewards with Predictive State Representations

Predictive state representations (PSRs) are models of controlled non-Mar...

0 Andrea Baisero, et al. ∙

research

∙ 06/07/2021

Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps

In robot navigation, generalizing quickly to unseen environments is esse...

0 Chengguang Xu, et al. ∙

research

∙ 05/25/2021

Unbiased Asymmetric Actor-Critic for Partially Observable Reinforcement Learning

In partially observable reinforcement learning, offline training gives a...

0 Andrea Baisero, et al. ∙

research

∙ 04/26/2021

End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

State-of-the-art human-in-the-loop robot grasping is hugely suffered by ...

0 Mohammadreza Sharif, et al. ∙

research

∙ 03/17/2021

Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones

Targets search and detection encompasses a variety of decision problems ...

0 Roi Yehoshua, et al. ∙

research

∙ 02/22/2021

Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning

Deep Reinforcement Learning (RL) methods rely on experience replay to ap...

0 Brett Daley, et al. ∙

research

∙ 02/08/2021

Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning

Centralized Training for Decentralized Execution, where agents are train...

0 Xueguang Lyu, et al. ∙

research

∙ 01/27/2021

Safe Multi-Agent Reinforcement Learning via Shielding

Multi-agent reinforcement learning (MARL) has been increasingly used in ...

0 Ingy Elsayed-Aly, et al. ∙

research

∙ 10/19/2020

Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability

Many important robotics problems are partially observable in the sense t...

6 Hai Nguyen, et al. ∙

research

∙ 10/03/2020

Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties

Many popular adaptive gradient methods such as Adam and RMSProp rely on ...

0 Brett Daley, et al. ∙

research

∙ 04/18/2020

Macro-Action-Based Deep Multi-Agent Reinforcement Learning

In real-world multi-robot systems, performing high-quality, collaborativ...

0 Yuchen Xiao, et al. ∙

research

∙ 09/24/2019

Active Goal Recognition

To coordinate with other systems, agents must be able to determine what ...

0 Chris Amato, et al. ∙

research

∙ 09/19/2019

Multi-Robot Deep Reinforcement Learning with Macro-Actions

In many real-world multi-robot tasks, high-quality solutions often requi...

0 Yuchen Xiao, et al. ∙

research

∙ 12/15/2018

On Improving Decentralized Hysteretic Deep Reinforcement Learning

Recent successes of value-based multi-agent deep reinforcement learning ...

0 Xueguang Lu, et al. ∙

research

∙ 11/14/2018

Bayesian Reinforcement Learning in Factored POMDPs

Bayesian approaches provide a principled solution to the exploration-exp...

0 Sammie Katt, et al. ∙

research

∙ 10/23/2018

Efficient Eligibility Traces for Deep Reinforcement Learning

Eligibility traces are an effective technique to accelerate reinforcemen...

0 Brett Daley, et al. ∙

research

∙ 06/14/2018

Learning in POMDPs with Monte Carlo Tree Search

The POMDP is a powerful framework for reasoning under outcome and inform...

0 Sammie Katt, et al. ∙

research

∙ 05/20/2018

Learning to Teach in Cooperative Multiagent Reinforcement Learning

We present a framework and algorithm for peer-to-peer teaching in cooper...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 10/17/2017

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

A key challenge in multi-robot and multi-agent systems is generating sol...

0 Trong Nghia Hoang, et al. ∙

research

∙ 07/24/2017

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

This paper presents a data-driven approach for multi-robot coordination ...

0 Miao Liu, et al. ∙

research

∙ 03/17/2017

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

Many real-world tasks involve multiple agents with partial observability...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 03/16/2017

Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces

This paper presents the first ever approach for solving continuous-obser...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 03/16/2017

Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations

Robust environment perception is essential for decision-making on robots...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 05/01/2015

Stick-Breaking Policy Learning in Dec-POMDPs

Expectation maximization (EM) has recently been shown to be an efficient...

0 Miao Liu, et al. ∙

research

∙ 02/20/2015

Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions

The focus of this paper is on solving multi-robot planning problems in c...

0 Shayegan Omidshafiei, et al. ∙

research

∙ 04/04/2014

Scalable Planning and Learning for Multiagent POMDPs: Extended Version

Online, sample-based planning algorithms for POMDPs have shown great pro...

0 Chris Amato, et al. ∙

research

∙ 02/12/2014

Planning for Decentralized Control of Multiple Robots Under Uncertainty

We describe a probabilistic framework for synthesizing control policies ...

0 Chris Amato, et al. ∙

research

∙ 02/04/2014

Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs

This article presents the state-of-the-art in optimal solution methods f...

0 Frans Adriaan Oliehoek, et al. ∙

research

∙ 01/15/2014

Policy Iteration for Decentralized Control of Markov Decision Processes

Coordination of distributed agents is required for problems arising in m...

0 Daniel S. Bernstein, et al. ∙

research

∙ 10/16/2012

Scaling Up Decentralized MDPs Through Heuristic Search

Decentralized partially observable Markov decision processes (Dec-POMDPs...

0 Jilles S. Dibangoye, et al. ∙

Chris Amato

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