Refinement of Soccer Agents' Positions Using Reinforcement Learning
RoboCup-97: Robot Soccer World Cup I
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning to Behave by Environment Reinforcement
RoboCup-99: Robot Soccer World Cup III
The RoboCup-98 Teamwork Evaluation Session: A Preliminary Report
RoboCup-99: Robot Soccer World Cup III
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On reinforcement learning with limited exploration, an agent's policy tends to fall into a worthless local optimum. This paper proposes Observational Reinforcement Learning method with which the learning agent evaluates inexperienced policies and reinforces it. This method provides the agent more chances to escape from a local optimum without exploration. Moreover, this paper shows the effectiveness of the method from experiments in the RoboCup positioning problem. They are advanced experiments described in our RoboCup-97 paper[1].