11 search results - page 2 / 3 » Model-Free Least-Squares Policy Iteration |
ICML
1999
IEEE
14 years 5 months ago
1999
IEEE
Excerpted from: Boyan, Justin. Learning Evaluation Functions for Global Optimization. Ph.D. thesis, Carnegie Mellon University, August 1998. (Available as Technical Report CMU-CS-...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least square...
ICMLA
2008
13 years 6 months ago
2008
In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data i...
ATAL
2009
Springer
13 years 11 months ago
2009
Springer
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
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...