Automatically assigned DDC number: 00631

Manually assigned DDC number: 00631

Number of references: 16

Title: Learning Team Strategies: Soccer Case Studies

Author:

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Subject: Marco A. Wiering,Michael Huhns,Gerhard Weiss Learning Team Strategies: Soccer Case Studies

Description: . We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy, but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare several learning algorithms: TD-Q learning with linear neural networks (TD-Q), Probabilistic Incremental Program Evolution (PIPE), and a PIPE version that learns by coevolution (CO-PIPE). TD-Q is based on learning evaluation functions (EFs) mapping input/action pairs to expected reward. PIPE and CO-PIPE search policy space directly. They use adaptive probability distributions to synthesize programs that calculate action probabilities from current inputs. Our results show that linear TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE and CO-PIPE, however, do not depend on EFs and find good policies faster and more reliably. This suggests that in s...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1997-12-16

Pubyear: 1998

Format: ps

Identifier: http://citeseer.ist.psu.edu/166643.html

Source: ftp://ftp.idsia.ch/pub/rafal/soccer.ps.gz

Language: en

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