'Action space' presentation slideshows

Chapter 8:

Chapter 8:. Seeing a Three-Dimensional World. The visual system must compute: . Depth (distance of an object from the perceiver) Egocentric direction (direction of an object relative to the perceiver). Allocentric frame of reference. Independent of the vantage point of a viewer. Examples:

724 views • 64 slides

Interaction Design Direct Manipulation

Interaction Design Direct Manipulation Prof. Dr. Matthias Rauterberg Faculty Industrial Design Technical University Eindhoven g.w.m.rauterberg@tue.nl 24-AUG-2005 Key references/literature: D.J. Mayhew (1992) Principles and guidelines in software user interface design. Prentice Hall.

1.15k views • 61 slides

619 views • 48 slides

Multiagent Coordination, Planning, Learning and Generalization with Factored MDPs

Multiagent Coordination, Planning, Learning and Generalization with Factored MDPs. Carlos Guestrin Daphne Koller Stanford University. Includes collaborations with: Geoffrey Gordon 1 , Michail Lagoudakis 2 , Ronald Parr 2 , Relu Patrascu 4 , Dale Schuurmans 4 , Shobha Venkataraman 3 ! 1

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Designing Cognitive Radio Networks to Yield Desired Behavior

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Designing Cognitive Radio Networks to Yield Desired Behavior

354 views • 35 slides

Automatic Induction of MAXQ Hierarchies

Automatic Induction of MAXQ Hierarchies. Neville Mehta Michael Wynkoop Soumya Ray Prasad Tadepalli Tom Dietterich School of EECS Oregon State University. Funded by DARPA Transfer Learning Program. Hierarchical Reinforcement Learning. Exploits domain structure to facilitate learning

431 views • 30 slides

Fitted/ batch /model-based RL: A (sketchy, biased) overview(?)

329 views • 26 slides

Reinforcement Learning with Multiple, Qualitatively Different State Representations

Reinforcement Learning with Multiple, Qualitatively Different State Representations. - TNO / UvA - UvA - TNO / UvA. Harm van Seijen Bram Bakker Leon Kester. The Reinforcement Learning Problem. action a. Environment. Agent. state s, reward r.

338 views • 24 slides

Hierarchy in the phase space and dark matter Astronomy

340 views • 22 slides

Collaboration with Spatial Aware UIs

Collaboration with Spatial Aware UIs. First Year Panel Roswitha Gostner. Outline. Introduction Motivation Related Work Preliminary Study Research Proposal Research Question Method & Workplan. Introduction Motivation Research Proposal. Introduction. Mobility of Computer

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Sample-based Planning for Continuous Action Markov Decision Processes

Sample-based Planning for Continuous Action Markov Decision Processes. Ari Weinstein Chris Mansley Michael L. Littman. aweinst@rutgers.edu cmansley@cs.rutgers.edu mlittman@cs.rutgers.edu. Rutgers Laboratory for Real-Life Reinforcement Learning. Motivation. Sample-based planning:

269 views • 16 slides

Sample-based Planning for Continuous Action Markov Decision Processes

Sample-based Planning for Continuous Action Markov Decision Processes. Ari Weinstein Chris Mansley Michael L. Littman. aweinst@rutgers.edu cmansley@cs.rutgers.edu mlittman@cs.rutgers.edu. Rutgers Laboratory for Real-Life Reinforcement Learning. Motivation. Sample-based planning:

167 views • 16 slides

Practical Reinforcement Learning in Continuous Space

Practical Reinforcement Learning in Continuous Space. William D. Smart Brown University Leslie Pack Kaelbling MIT. Presented by: David LeRoux. Goals of Paper. Practical RL approach Handles continuous state and action spaces Safely approximates value function

190 views • 6 slides