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ICML
2009
IEEE
14 years 8 months ago
Binary action search for learning continuous-action control policies
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
Jason Pazis, Michail G. Lagoudakis
ICANN
2001
Springer
13 years 12 months ago
Market-Based Reinforcement Learning in Partially Observable Worlds
Unlike traditional reinforcement learning (RL), market-based RL is in principle applicable to worlds described by partially observable Markov Decision Processes (POMDPs), where an ...
Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
ECML
2007
Springer
13 years 11 months ago
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
CEC
2007
IEEE
14 years 1 months ago
Combine and compare evolutionary robotics and reinforcement Learning as methods of designing autonomous robots
—The purpose of this paper is to present a comparison between two methods of building adaptive controllers for robots. In spite of the wide range of techniques which are used for...
Sergiu Goschin, Eduard Franti, Monica Dascalu, San...
ICRA
2009
IEEE
227views Robotics» more  ICRA 2009»
14 years 2 months ago
Adaptive autonomous control using online value iteration with gaussian processes
— In this paper, we present a novel approach to controlling a robotic system online from scratch based on the reinforcement learning principle. In contrast to other approaches, o...
Axel Rottmann, Wolfram Burgard