3 citations · 6 across the 13 of their papers we have counts for
6 papers · 1 filter
Constrained Multi-Agent Reinforcement Learning in Imagination
Raz Shmueli, Shahaf S. Shperberg
Predicting cost in a multi-agent world model moves most constrained policy updates into imagination: the stronger model-free baseline needs to times CA-MAMBA's interactio…
Optimal Planning in a Dynamic World
Devin Wild Thomas, Solomon Eyal Shimony, Wheeler Ruml +3
Background: We address the problem of planning when the set of feasible states or actions changes over time. For example, in the problem of path planning among moving obstacles (so…
StochSIPP: Safe Interval Path Planning in Stochastic Dynamic Environments
Ajith Kemisetti, Shahaf S. Shperberg, Yoonchang Sung
Safe navigation under uncertain time-dependent blockage requires anticipating observations before committing to motion. We present StochSIPP, an exact contingent planner for tempor…
Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics
Tzur Shubi, Ariel Felner, Solomon Eyal Shimony +1
Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search. Therein, it is well-known that front-to-front heuristics can reduce th…
RAMP: Hybrid DRL for Online Learning of Numeric Action Models
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
Automated planning algorithms require an action model specifying the preconditions and effects of each action, but obtaining such a model is often hard. Learning action models from…
From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
Lidor Erez, Shahaf S. Shperberg, Ayal Taitler
In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission. Such problems are inherently mixed discrete-continuous: a high-level action sequenc…