8 citations · 13 across the 2 of their papers we have counts for
6 papers · 1 filter
PDDLGym: Gym Environments from PDDL Problems
Tom Silver, Rohan Chitnis
We present PDDLGym, a framework that automatically constructs OpenAI Gym environments from PDDL domains and problems. Observations and actions in PDDLGym are relational, making the…
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
Rohan Chitnis, Tom Silver, Joshua Tenenbaum +2
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspi…
Learning Quickly to Plan Quickly Using Modular Meta-Learning
Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators. The e…
Finding Frequent Entities in Continuous Data
Ferran Alet, Rohan Chitnis, Leslie P. Kaelbling +1
In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of detections. Rathe…
Learning What Information to Give in Partially Observed Domains
Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
In many robotic applications, an autonomous agent must act within and explore a partially observed environment that is unobserved by its human teammate. We consider such a setting…
Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization
Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
In partially observed environments, it can be useful for a human to provide the robot with declarative information that represents probabilistic relational constraints on propertie…