74 citations · 279 across the 24 of their papers we have counts for
6 papers · 1 filter
Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed Environments
Hari Teja Tatavarti, Prashant Doshi, Layton Hayes
Recent investigations into sum-product-max networks (SPMN) that generalize sum-product networks (SPN) offer a data-driven alternative for decision making, which has predominantly r…
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic
Tomoki Nishi, Prashant Doshi, Danil Prokhorov
Freeway merging in congested traffic is a significant challenge toward fully automated driving. Merging vehicles need to decide not only how to merge into a spot, but also where to…
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics
Tomoki Nishi, Prashant Doshi, Michael R. James +1
In many robotic applications, some aspects of the system dynamics can be modeled accurately while others are difficult to obtain or model. We present a novel reinforcement learning…
Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams
Yifeng Zeng, Prashant Doshi
We focus on the problem of sequential decision making in partially observable environments shared with other agents of uncertain types having similar or conflicting objectives. Thi…
Monte Carlo Sampling Methods for Approximating Interactive POMDPs
Prashant Doshi, Piotr J. Gmytrasiewicz
Partially observable Markov decision processes (POMDPs) provide a principled framework for sequential planning in uncertain single agent settings. An extension of POMDPs to multiag…
A Framework for Sequential Planning in Multi-Agent Settings
P. Doshi, P. J. Gmytrasiewicz
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space…