181 search results - page 20 / 37 » State Space Reduction For Hierarchical Reinforcement Learnin... |
ECML
2004
Springer
14 years 26 days ago
2004
Springer
Given the pattern-based multi-predictors of the stock price, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset ...
ICANNGA
2007
Springer
14 years 1 months ago
2007
Springer
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
NN
2007
Springer
13 years 7 months ago
2007
Springer
Reinforcement learning is based on exploration of the environment and receiving reward that indicates which actions taken by the agent are good and which ones are bad. In many app...
IJCAI
2007
13 years 9 months ago
2007
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
IROS
2009
IEEE
14 years 2 months ago
2009
IEEE
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...