Planning and learning at multiple levels of temporal abstraction is a key problem for artificial intelligence. In this paper we summarize an ap-.
Multi-time Models for Temporally Abstract Planning. Part of Advances in Neural Information Processing Systems 10 (NIPS 1997) · Bibtex Metadata Paper. Authors.
Dec 1, 1997 · This paper generalizes prior work on temporally abstract models [Sutton, 1995] and extends it from the prediction setting to include actions, ...
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key challenges for AI. In this paper we develop an approach to ...
A more general form of temporally abstract model is introduced, the multi-time model, and its suitability for planning and learning by virtue of its ...
We present new theoretical results on planning within the framework of temporally abstract reinforcement learning (Precup & Sutton, 1997; Sutton, 1995).
Abstract: Planning and learning at multiple levels of temporal abstraction is a key problem for artificial intelligence. In this paper we summarize an approach ...
Multi-time models for temporally abstract planning. In Advances in Neural Information Processing Systems 10 (Proceedings of NIPS'97), pages. 1050–1056. MIT ...
Thus, this paper focuses on the external attractiveness of the company. To attract candidates, the company needs to focus on improving their brand by ...
Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement learning, option ...