5 citations · 6 across the 3 of their papers we have counts for
4 papers
Inverse Reinforcement Learning Under Noisy Observations
Shervin Shahryari, Prashant Doshi
We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, a noisy continuous-time ob…
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…
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs
Ekhlas Sonu, Yingke Chen, Prashant Doshi
Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating…
From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data
Amir H. Asiaee, Prashant Doshi, Todd Minning +4
We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-ba…