50 search results - page 10 / 10 » Reinforcement Learning for Penalty Avoidance in Continuous S... |
ICML
2001
IEEE
14 years 6 months ago
2001
IEEE
Direct policy search is a practical way to solve reinforcement learning problems involving continuous state and action spaces. The goal becomes finding policy parameters that maxi...
AAMAS
2007
Springer
13 years 11 months ago
2007
Springer
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that...
MIRRORBOT
2005
Springer
13 years 10 months ago
2005
Springer
Abstract. A biologically inspired computational model of rodent representation–based (locale) navigation is presented. The model combines visual input in the form of realistic tw...
AAAI
2006
13 years 6 months ago
2006
In multiple criteria Markov Decision Processes (MDP) where multiple costs are incurred at every decision point, current methods solve them by minimising the expected primary cost ...
ICML
2010
IEEE
13 years 6 months ago
2010
IEEE
We present new algorithms for inverse optimal control (or inverse reinforcement learning, IRL) within the framework of linearlysolvable MDPs (LMDPs). Unlike most prior IRL algorit...