Probabilistic Relational Planning with First Order Decision Diagrams

2014-01-16 · Saket Joshi, Roni Khardon

Dynamic programming algorithms have been successfully applied to propositional stochastic planning problems by using compact representations, in particular algebraic decision diagrams, to capture domain dynamics and value functions. Work on symbolic dynamic programming lifted these ideas to first order logic using several representation schemes. Recent work introduced a first order variant of decision diagrams (FODD) and developed a value iteration algorithm for this representation. This paper develops several improvements to the FODD algorithm that make the approach practical. These include, new reduction operators that decrease the size of the representation, several speedup techniques, and techniques for value approximation. Incorporating these, the paper presents a planning system, FODD-Planner, for solving relational stochastic planning problems. The system is evaluated on several domains, including problems from the recent international planning competition, and shows competitive performance with top ranking systems. This is the first demonstration of feasibility of this approach and it shows that abstraction through compact representation is a promising approach to stochastic planning.

📄 PDF Abstract BibTeX arXiv:1401.3882

Code (0)

등록된 구현이 없습니다.

Generalized Neural Policies for Relational MDPs

2020-01-01 · ICML 2020 1 · Sankalp Garg, Aniket Bajpai, Mausam

A Relational Markov Decision Process (RMDP) is a first-order representation to express all instances of a single probabilistic planning domain with possibly unbounded number of objects. Early work in RMDPs outputs gener…

Planning with Noisy Probabilistic Relational Rules

2014-01-16 · Tobias Lang, Marc Toussaint

Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually interpretable and they can be learned effec…

Robot Manipulation

Symbolic Network: Generalized Neural Policies for Relational MDPs

2020-02-18 · Sankalp Garg, Aniket Bajpai, Mausam

Lifted Model Checking for Relational MDPs

2021-06-22 · Wen-Chi Yang, Jean-François Raskin, Luc De Raedt

Probabilistic model checking has been developed for verifying systems that have stochastic and nondeterministic behavior. Given a probabilistic system, a probabilistic model checker takes a property and checks whether or…

model Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

Automatic Induction of Bellman-Error Features for Probabilistic Planning

2014-01-16 · Jia-Hong Wu, Robert Givan

Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provided, domain-independent algorithms such as a…