Shlomo Zilberstein

research

∙ 06/28/2023

RL^3: Boosting Meta Reinforcement Learning via RL inside RL^2

Meta reinforcement learning (meta-RL) methods such as RL^2 have emerged ...

0 Abhinav Bhatia, et al. ∙

research

∙ 06/06/2022

Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL

Model-based reinforcement learning promises to learn an optimal policy f...

2 Abhinav Bhatia, et al. ∙

research

∙ 06/01/2022

Dense Crowd Flow-Informed Path Planning

Both pedestrian and robot comfort are of the highest priority whenever a...

0 Emily Pruc, et al. ∙

research

∙ 05/30/2022

A Unifying Framework for Causal Explanation of Sequential Decision Making

We present a novel framework for causal explanations of stochastic, sequ...

0 Samer B. Nashed, et al. ∙

research

∙ 09/28/2021

Competence-Aware Path Planning via Introspective Perception

Robots deployed in the real world over extended periods of time need to ...

1 Sadegh Rabiee, et al. ∙

research

∙ 08/01/2021

Agent-aware State Estimation in Autonomous Vehicles

Autonomous systems often operate in environments where the behavior of m...

6 Shane Parr, et al. ∙

research

∙ 02/13/2021

Mitigating Negative Side Effects via Environment Shaping

Agents operating in unstructured environments often produce negative sid...

6 Sandhya Saisubramanian, et al. ∙

research

∙ 02/08/2021

Learning to Generate Fair Clusters from Demonstrations

Fair clustering is the process of grouping similar entities together, wh...

3 Sainyam Galhotra, et al. ∙

research

∙ 10/10/2020

Helpfulness as a Key Metric of Human-Robot Collaboration

As robotic teammates become more common in society, people will assess t...

0 Richard G. Freedman, et al. ∙

research

∙ 08/24/2020

Avoiding Negative Side Effects due to Incomplete Knowledge of AI Systems

Autonomous agents acting in the real-world often operate based on models...

10 Sandhya Saisubramanian, et al. ∙

research

∙ 07/23/2020

Improving Competence for Reliable Autonomy

Given the complexity of real-world, unstructured domains, it is often im...

0 Connor Basich, et al. ∙

research

∙ 03/17/2020

Learning to Optimize Autonomy in Competence-Aware Systems

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradi...

4 Connor Basich, et al. ∙

research

∙ 12/17/2019

Balancing the Tradeoff Between Clustering Value and Interpretability

Graph clustering groups entities – the vertices of a graph – based on th...

16 Sandhya Saisubramanian, et al. ∙

research

∙ 09/13/2019

Responsive Planning and Recognition for Closed-Loop Interaction

Many intelligent systems currently interact with others using at least o...

18 Richard G. Freedman, et al. ∙

research

∙ 05/22/2019

Minimizing the Negative Side Effects of Planning with Reduced Models

Reduced models of large Markov decision processes accelerate planning by...

1 Sandhya Saisubramanian, et al. ∙

research

∙ 03/02/2019

Lexicographically Ordered Multi-Objective Clustering

We introduce a rich model for multi-objective clustering with lexicograp...

12 Sainyam Galhotra, et al. ∙

research

∙ 10/18/2018

Planning in Stochastic Environments with Goal Uncertainty

We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -...

0 Sandhya Saisubramanian, et al. ∙

research

∙ 02/16/2018

An Anytime Algorithm for Task and Motion MDPs

Integrated task and motion planning has emerged as a challenging problem...

0 Siddharth Srivastava, et al. ∙

research

∙ 05/21/2017

Generalizing the Role of Determinization in Probabilistic Planning

The stochastic shortest path problem (SSP) is a highly expressive model ...

0 Luis Pineda, et al. ∙

research

∙ 12/01/2016

Robust Optimization for Tree-Structured Stochastic Network Design

Stochastic network design is a general framework for optimizing network ...

0 Xiaojian Wu, et al. ∙

research

∙ 01/15/2014

A Bilinear Programming Approach for Multiagent Planning

Multiagent planning and coordination problems are common and known to be...

0 Marek Petrik, 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

∙ 01/16/2013

The Complexity of Decentralized Control of Markov Decision Processes

Planning for distributed agents with partial state information is consid...

0 Daniel S. Bernstein, et al. ∙

research

∙ 10/19/2012

Symbolic Generalization for On-line Planning

Symbolic representations have been used successfully in off-line plannin...

0 Zhengzhu Feng, et al. ∙

research

∙ 07/11/2012

Region-Based Incremental Pruning for POMDPs

We present a major improvement to the incremental pruning algorithm for ...

0 Zhengzhu Feng, et al. ∙

research

∙ 07/04/2012

MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

We present multi-agent A* (MAA*), the first complete and optimal heurist...

0 Daniel Szer, et al. ∙

research

∙ 03/15/2012

Rollout Sampling Policy Iteration for Decentralized POMDPs

We present decentralized rollout sampling policy iteration (DecRSPI) - a...

0 Feng Wu, et al. ∙

research

∙ 03/15/2012

Anytime Planning for Decentralized POMDPs using Expectation Maximization

Decentralized POMDPs provide an expressive framework for multi-agent seq...

0 Akshat Kumar, et al. ∙

research

∙ 02/14/2012

Message-Passing Algorithms for Quadratic Programming Formulations of MAP Estimation

Computing maximum a posteriori (MAP) estimation in graphical models is a...

0 Akshat Kumar, et al. ∙

research

∙ 10/31/2011

Communication-Based Decomposition Mechanisms for Decentralized MDPs

Multi-agent planning in stochastic environments can be framed formally a...

0 Claudia V. Goldman, et al. ∙

research

∙ 05/11/2010

Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes

Approximate dynamic programming has been used successfully in a large va...

0 Marek Petrik, et al. ∙

Shlomo Zilberstein

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