Prashant Doshi | DeepAI

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∙ 05/09/2023

Latent Interactive A2C for Improved RL in Open Many-Agent Systems

There is a prevalence of multiagent reinforcement learning (MARL) method...

0 Keyang He, et al. ∙

∙ 08/15/2022

IRL with Partial Observations using the Principle of Uncertain Maximum Entropy

The principle of maximum entropy is a broadly applicable technique for c...

0 Kenneth Bogert, et al. ∙

∙ 02/22/2022

SIPOMDPLite-Net: Lightweight, Self-Interested Learning and Planning in POSGs with Sparse Interactions

This work introduces sIPOMDPLite-net, a deep neural network (DNN) archit...

0 Gengyu Zhang, et al. ∙

∙ 09/16/2021

Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noise

We consider the problem of learning the behavioral preferences of an exp...

0 Prasanth Sengadu Suresh, et al. ∙

∙ 07/13/2021

A Hierarchical Bayesian model for Inverse RL in Partially-Controlled Environments

Robots learning from observations in the real world using inverse reinfo...

0 Kenneth Bogert, et al. ∙

∙ 06/17/2021

Many Agent Reinforcement Learning Under Partial Observability

Recent renewed interest in multi-agent reinforcement learning (MARL) has...

0 Keyang He, et al. ∙

∙ 10/15/2020

Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards

Consider a typical organization whose worker agents seek to collectively...

0 Keyang He, et al. ∙

∙ 07/18/2020

Active Deception using Factored Interactive POMDPs to Recognize Cyber Attacker's Intent

This paper presents an intelligent and adaptive agent that employs decep...

0 Aditya Shinde, et al. ∙

∙ 06/12/2020

Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed Environments

Recent investigations into sum-product-max networks (SPMN) that generali...

0 Hari Teja Tatavarti, et al. ∙

∙ 04/27/2020

Maximum Entropy Multi-Task Inverse RL

Multi-task IRL allows for the possibility that the expert could be switc...

0 Saurabh Arora, et al. ∙

∙ 11/20/2019

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

In open agent systems, the set of agents that are cooperating or competi...

0 Adam Eck, et al. ∙

∙ 09/03/2019

GrAALF:Supporting Graphical Analysis of Audit Logs for Forensics

System-call level audit logs often play a critical role in computer fore...

0 Omid Setayeshfar, et al. ∙

∙ 05/10/2019

SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams

Learning from demonstration (LfD) and imitation learning offer new parad...

0 Nihal Soans, et al. ∙

∙ 06/18/2018

A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress

Inverse reinforcement learning is the problem of inferring the reward fu...

8 Saurabh Arora, et al. ∙

∙ 05/23/2018

Reinforcement Learning for Heterogeneous Teams with PALO Bounds

We introduce reinforcement learning for heterogeneous teams in which rew...

0 Roi Ceren, et al. ∙

∙ 05/21/2018

A Framework and Method for Online Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the problem of learning the pref...

0 Saurabh Arora, et al. ∙

∙ 10/27/2017

Inverse Reinforcement Learning Under Noisy Observations

We consider the problem of performing inverse reinforcement learning whe...

0 Shervin Shahryari, et al. ∙

∙ 07/14/2017

Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic

Freeway merging in congested traffic is a significant challenge toward f...

0 Tomoki Nishi, et al. ∙

∙ 06/04/2017

Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics

In many robotic applications, some aspects of the system dynamics can be...

0 Tomoki Nishi, et al. ∙

∙ 11/13/2015

Dynamic Sum Product Networks for Tractable Inference on Sequence Data (Extended Version)

Sum-Product Networks (SPN) have recently emerged as a new class of tract...

0 Mazen Melibari, et al. ∙

∙ 03/24/2015

Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

Interactive partially observable Markov decision processes (I-POMDP) pro...

0 Ekhlas Sonu, et al. ∙

∙ 09/01/2014

Team Behavior in Interactive Dynamic Influence Diagrams with Applications to Ad Hoc Teams

Planning for ad hoc teamwork is challenging because it involves agents c...

0 Muthukumaran Chandrasekaran, et al. ∙

∙ 01/18/2014

Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams

We focus on the problem of sequential decision making in partially obser...

0 Yifeng Zeng, et al. ∙

∙ 01/15/2014

Monte Carlo Sampling Methods for Approximating Interactive POMDPs

Partially observable Markov decision processes (POMDPs) provide a princi...

0 Prashant Doshi, et al. ∙

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