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» Solving Deep Memory POMDPs with Recurrent Policy Gradients
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ICANN
2007
Springer
14 years 1 months ago
Solving Deep Memory POMDPs with Recurrent Policy Gradients
Abstract. This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov...
Daan Wierstra, Alexander Förster, Jan Peters,...
GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
14 years 1 months ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
ICANN
2010
Springer
13 years 7 months ago
Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients
Abstract. Developing superior artificial board-game players is a widelystudied area of Artificial Intelligence. Among the most challenging games is the Asian game of Go, which, des...
Mandy Grüttner, Frank Sehnke, Tom Schaul, J&u...
ECML
2007
Springer
14 years 1 months ago
Policy Gradient Critics
We present Policy Gradient Actor-Critic (PGAC), a new model-free Reinforcement Learning (RL) method for creating limited-memory stochastic policies for Partially Observable Markov ...
Daan Wierstra, Jürgen Schmidhuber
JMLR
2010
227views more  JMLR 2010»
13 years 6 months ago
PyBrain
PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easyto-use yet still powerful algorithms for machine learning tasks, including a vari...
Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, M...