67 search results - page 6 / 14 » Learning predictive state representations using non-blind po... |
AAMAS
2002
Springer
13 years 7 months ago
2002
Springer
In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it ...
ACSC
2008
IEEE
13 years 9 months ago
2008
IEEE
Recurrent neural networks are able to store information about previous as well as current inputs. This "memory" allows them to solve temporal problems such as language r...
RSS
2007
13 years 9 months ago
2007
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
AIPS
2007
13 years 10 months ago
2007
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
TCAD
2010
13 years 2 months ago
2010
- This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state...