|
|
|
|
|
|
|
|
|
|
| |
| Duration |
|
2003-2005
|
| Funding |
|
SNF
|
|
| Partners |
|
|
|
| |
|
|
|
|
|
|
| |
|
|
ORAUE: Optimal Rational Agents in Unknown Environments
|
Goal of this project is to extend and deepen a recent theory of
theoretically optimal universal agents interacting with unknown
environments. We build on
Solomonoff's
celebrated universal theory of induction to derive an optimal
reinforcement learning agent, called AIXI, embedded in a world
whose responses to the agent's actions are sampled from a
computable probability distribution -- this is the only very weak
assumption.
From an algorithmic complexity perspective, the AIXI model
generalizes optimal passive universal induction to the case of
active agents. From a decision theoretic perspective, the AIXI
model is a suggestion of a new (implicit) "learning" algorithm,
which may overcome all (except computational) problems of previous
reinforcement learning algorithms. If the optimality theorems of
universal induction and decision theory generalize to the unified
AIXI model, we would have, for the first time, a universal
(parameterless) model of an optimal rational agent in any
computable but unknown environment with reinforcement feedback.
Keywords: algorithmic complexity, sequential decision
theory, induction, time series prediction, reinforcement
learning, strategic games, function minimization, supervised
learning.
|
| People |
Jan Poland
Shane Legg
|
|
Coordinator
|
Marcus Hutter
|
|
|
|
|
|
home |
people |
jobs |
projects |
tech reports |
contact |
links |
talks
|
| |
Copyright © 2003 - 2012 IDSIA. All Rights Reserved.
|
