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ICML
2009
IEEE
16 years 18 days ago
Approximate inference for planning in stochastic relational worlds
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
Tobias Lang, Marc Toussaint
AIPS
2007
15 years 2 months ago
Transformational Planning for Everyday Activity
We propose an approach to transformational planning and learning of everyday activity. This approach is targeted at autonomous robots that are to perform complex activities such a...
Armin Müller, Alexandra Kirsch, Michael Beetz
NIPS
1993
15 years 1 months ago
Robust Reinforcement Learning in Motion Planning
While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...
Satinder P. Singh, Andrew G. Barto, Roderic A. Gru...
AUSAI
2008
Springer
15 years 1 months ago
Partial Order Hierarchical Reinforcement Learning
In this paper the notion of a partial-order plan is extended to task-hierarchies. We introduce the concept of a partial-order taskhierarchy that decomposes a problem using multi-ta...
Bernhard Hengst
ICRA
2005
IEEE
118views Robotics» more  ICRA 2005»
15 years 5 months ago
Learning-Assisted Multi-Step Planning
— Probabilistic sampling-based motion planners are unable to detect when no feasible path exists. A common heuristic is to declare a query infeasible if a path is not found in a ...
Kris K. Hauser, Timothy Bretl, Jean-Claude Latombe