1236 search results - page 56 / 248 » Opposition-Based Reinforcement Learning |
102
click to vote
ECML
2006
Springer
15 years 4 months ago
2006
Springer
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
104
Voted
ML
1998
ACM
15 years 12 days ago
1998
ACM
Following Tesauro’s work on TD-Gammon, we used a 4000 parameter feed-forward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of t...
125
click to vote
MAGS
2010
14 years 7 months ago
2010
Coordinating agents in a complex environment is a hard problem, but it can become even harder when certain characteristics of the tasks, like the required number of agents, are un...
86
Voted
PRIMA
2009
Springer
15 years 7 months ago
2009
Springer
In this article, we propose a method to adapt stepsize parameters used in reinforcement learning for dynamic environments. In general reinforcement learning situations, a stepsize...
94
Voted
ACL
2010
14 years 10 months ago
2010
In this paper, we address the task of mapping high-level instructions to sequences of commands in an external environment. Processing these instructions is challenging--they posit...