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In RL, environments are usually distinguished into episodic and non-episodic tasks. An episode can terminate when a terminal condition is met, e.g., the fall of a walking humanoid or reaching a pre-specified number of time steps. Non-episodic (continuing) tasks, in contrast, do not have a certain ending point, and the agent roams the environment on an infinite horizon. Since the BS performs beamforming and configures RIS elements continuously, it is evident that this is a continuing task by nature. While it is possible to specify a set of terminal conditions such as a sum rate threshold or a maximum number of steps, it might introduce bias and mislead the learning agents. Therefore, we adopt the continuing task framework to develop our approach.
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IEEE/ICACT20240375 Slide.13
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