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While many works were devoted to validating its efficacy or improving its performance, few discuss the feasibility of the DRL agent deployment for embedded computing. State-of-the-art approaches focus on the miniaturization of agents’ inferential networks, such as pruning and quantization, to minimize their energy and resource consumption. However, this spatial-based paradigm still proves inadequate for resource-stringent systems. In this paper, we address the feasibility from a temporal perspective, where FiDRL, a flexible invocation-based DRL model is proposed to judiciously invoke itself to minimize the overall system energy consumption, given that the DRL agent incurs non-negligible energy overhead during invocations. Our approach is three-fold: (1) FiDRL that extends DRL by incorporating the agent's invocation interval into the action space to achieve invocation flexibility; (2) a FiDRL-based DVFS approach for both inter- and intra-task scheduling that minimizes the overall execution energy consumption; and (3) a FiDRL-based DVFS platform design and an on\u002Foff-chip hybrid algorithm specialized for training the DRL agent for embedded systems. Experiment results show that FiDRL achieves 55.1% agent invocation cost reduction, under 23.3% overall energy reduction, compared to state-of-the-art approaches.",[11,12,13,14,15,16,17,18,19,20],"Li, Jingjin","Jiang, Weixiong","He, Yuting","Yang, Qingyu","Gao, Anqi","Ha, Yajun","Özcan, Ender","Bai, Ruibin","Cui, Tianxiang","Yu, Heng",[22,23,24,25,26,27,28,26,29,30],"0000-0001-7248-5180","0000-0002-6014-6453","0000-0002-1018-1912","0000-0001-9375-0457",null,"0000-0003-4244-5916","0000-0003-0276-1391","0000-0002-0102-2581","0000-0002-0305-2135",2024,"https:\u002F\u002Fdoi.org\u002F10.1109\u002Ftc.2024.3465933","W4402743254","IEEE Transactions on Computers",[36,37,38,39,40,41,42,43,44],"Computer science","Reinforcement learning","Invocation","Scheduling (production processes)","Parallel computing","Distributed computing","Computer architecture","Artificial 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