Search Results for author:
Found 5 papers, 0 papers with code
SparseDice: Imitation Learning for Temporally Sparse Data via Regularization
We are concerned with a setting where the demonstrations comprise only a subset of state-action pairs (as opposed to the whole trajectories).
Disentangled Planning and Control in Vision Based Robotics via Reward Machines
In this work we augment a Deep Q-Learning agent with a Reward Machine (DQRM) to increase speed of learning vision-based policies for robot tasks, and overcome some of the limitations of DQN that prevent it from converging to good-quality policies.
Towards Neural-Guided Program Synthesis for Linear Temporal Logic Specifications
Synthesizing a program that realizes a logical specification is a classical problem in computer science.
Finite LTL Synthesis with Environment Assumptions and Quality Measures
In this paper, we investigate the problem of synthesizing strategies for linear temporal logic (LTL) specifications that are interpreted over finite traces -- a problem that is central to the automated construction of controllers, robot programs, and business processes.
Finite LTL Synthesis is EXPTIME-complete
LTL synthesis -- the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applications.
