32 search results - page 3 / 7 » Kernel-Based Reinforcement Learning on Representative States |
NIPS
2001
14 years 10 months ago
2001
We address two open theoretical questions in Policy Gradient Reinforcement Learning. The first concerns the efficacy of using function approximation to represent the state action ...
COLT
1994
Springer
15 years 1 months ago
1994
Springer
Realistic domains for learning possess regularities that make it possible to generalize experience across related states. This paper explores an environment-modeling framework tha...
ICPR
2006
IEEE
15 years 10 months ago
2006
IEEE
To accelerate the learning of reinforcement learning, many types of function approximation are used to represent state value. However function approximation reduces the accuracy o...
JCP
2008
14 years 9 months ago
2008
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...
AIIDE
2006
14 years 11 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...