Teamwork Formation for Keepaway in Robotics Soccer (Reinforcement Learning Approach)

Tanaka, Nobuyuki; Arai, Sachiyo

doi:10.1007/11802372_28

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Simulation Environment1

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2007

2023

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Cited by 3 publications

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“…We show the initial positions of these agents in figure 4. We know the Keepaway task [12], [15] as another soccer game. We do not use the task since the performance strongly depends on the designing of a reward and penalty.…”

Section: Simulation Environmentmentioning

confidence: 99%

Proposal and evaluation of the penalty avoiding rational policy making algorithm with penalty level

Miyazaki

Kojima

Kobayashi

2007

SICE Annual Conference 2007

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Reinforcement learning (RL) is a kind of machine learning. It aims to adapt an agent to a given environment by utilizing a reward and a penalty. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [5] and the Penalty Avoiding Profit Sharing (PAPS) [6] as examples of RL systems that are able to suppress a penalty and learn a rational policy. However they cannot treat multiple penalties. In this paper, we extend PARP/PAPS to the environments where there are some kinds of penalties. We propose the Penalty Avoiding Rational Policy Making Algorithm with Penalty Level (PARP L ) that can control how to avoid penalties. We show the effectiveness of PARP L by soccer game simulations.

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Section: Simulation Environmentmentioning

confidence: 99%