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07/22/2023
On-Robot Bayesian Reinforcement Learning for POMDPs
Robot learning is often difficult due to the expense of gathering data. ...
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02/20/2023
Improving Deep Policy Gradients with Value Function Search
Deep Policy Gradient (PG) algorithms employ value networks to drive the ...
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02/20/2023
Safe Deep Reinforcement Learning by Verifying Task-Level Properties
Cost functions are commonly employed in Safe Deep Reinforcement Learning...
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01/26/2023
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Off-policy learning from multistep returns is crucial for sample-efficie...
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11/03/2022
Leveraging Fully Observable Policies for Learning under Partial Observability
Reinforcement learning in partially observable domains is challenging du...
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09/20/2022
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Synchronizing decisions across multiple agents in realistic settings is ...
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06/02/2022
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
Real-world reinforcement learning tasks often involve some form of parti...
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04/02/2022
Hierarchical Reinforcement Learning under Mixed Observability
The framework of mixed observable Markov decision processes (MOMDP) mode...
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02/17/2022
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
While reinforcement learning (RL) has made great advances in scalability...
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01/03/2022
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
Centralized Training for Decentralized Execution, where training is done...
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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...
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12/06/2021
Virtual Replay Cache
Return caching is a recent strategy that enables efficient minibatch tra...
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11/01/2021
Human-Level Control without Server-Grade Hardware
Deep Q-Network (DQN) marked a major milestone for reinforcement learning...
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10/16/2021
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning
Policy gradient methods have become popular in multi-agent reinforcement...
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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...
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06/07/2021
Reconciling Rewards with Predictive State Representations
Predictive state representations (PSRs) are models of controlled non-Mar...
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06/07/2021
Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps
In robot navigation, generalizing quickly to unseen environments is esse...
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05/25/2021
Unbiased Asymmetric Actor-Critic for Partially Observable Reinforcement Learning
In partially observable reinforcement learning, offline training gives a...
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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 ...
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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 ...
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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...
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02/08/2021
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning
Centralized Training for Decentralized Execution, where agents are train...
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01/27/2021
Safe Multi-Agent Reinforcement Learning via Shielding
Multi-agent reinforcement learning (MARL) has been increasingly used in ...
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10/19/2020
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability
Many important robotics problems are partially observable in the sense t...
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10/03/2020
Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties
Many popular adaptive gradient methods such as Adam and RMSProp rely on ...
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04/18/2020
Macro-Action-Based Deep Multi-Agent Reinforcement Learning
In real-world multi-robot systems, performing high-quality, collaborativ...
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09/24/2019
Active Goal Recognition
To coordinate with other systems, agents must be able to determine what ...
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09/19/2019
Multi-Robot Deep Reinforcement Learning with Macro-Actions
In many real-world multi-robot tasks, high-quality solutions often requi...
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12/15/2018
On Improving Decentralized Hysteretic Deep Reinforcement Learning
Recent successes of value-based multi-agent deep reinforcement learning ...
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11/14/2018
Bayesian Reinforcement Learning in Factored POMDPs
Bayesian approaches provide a principled solution to the exploration-exp...
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10/23/2018
Efficient Eligibility Traces for Deep Reinforcement Learning
Eligibility traces are an effective technique to accelerate reinforcemen...
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06/14/2018
Learning in POMDPs with Monte Carlo Tree Search
The POMDP is a powerful framework for reasoning under outcome and inform...
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05/20/2018
Learning to Teach in Cooperative Multiagent Reinforcement Learning
We present a framework and algorithm for peer-to-peer teaching in cooper...
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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...
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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 ...
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03/17/2017
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Many real-world tasks involve multiple agents with partial observability...
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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...
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03/16/2017
Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations
Robust environment perception is essential for decision-making on robots...
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05/01/2015
Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient...
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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...
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04/04/2014
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great pro...
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02/12/2014
Planning for Decentralized Control of Multiple Robots Under Uncertainty
We describe a probabilistic framework for synthesizing control policies ...
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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...
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01/15/2014
Policy Iteration for Decentralized Control of Markov Decision Processes
Coordination of distributed agents is required for problems arising in m...
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10/16/2012
