Autonomous Learning in a Pseudo-Episodic Physical Environment

نویسندگان

چکیده

Abstract Forpractical considerations reinforcement learning has proven to be a difficult task outside of simulation when applied physical experiment. Here we derive an optional approach model free learning, achieved entirely online, through careful experimental design and algorithmic decision making. We scheme implement traditionally episodic algorithms for unstable 1-dimensional mechanical environment. The training is completely autonomous, requiring no human present throughout the process. show that pseudo-episodic technique allows additional updates with off-policy actor-critic experience replay methods. including these between periods traditional episodes can improve speed consistency learning. Furthermore, validate procedure in hardware. In environment, several algorithm variants learned rapidly, each surpassing baseline maximum reward. this research are use only information obtained by onboard sensor during training.

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ژورنال

عنوان ژورنال: Journal of Intelligent and Robotic Systems

سال: 2022

ISSN: ['1573-0409', '0921-0296']

DOI: https://doi.org/10.1007/s10846-022-01577-5