1 citations · 2 across the 6 of their papers we have counts for
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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…
Toward PDDL Planning Copilot
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
Large Language Models (LLMs) are increasingly being used as autonomous agents capable of performing complicated tasks. However, they lack the ability to perform reliable long-horiz…
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks
Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg +1
Automated Planning algorithms require a model of the domain that specifies the preconditions and effects of each action. Obtaining such a domain model is notoriously hard. Algorith…
Safe Learning of PDDL Domains with Conditional Effects -- Extended Version
Argaman Mordoch, Enrico Scala, Roni Stern +1
Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in s…