Multidevice cooperative wireless sensing offers a promising solution for human motion recognition, owing to its superior privacy preservation and robustness. In the sensing process, devices continuously extract features from channel echoes and transmit them to a fusion center for motion recognition over successive time slots. The intertwined sub-processes of sensing and communication jointly determine recognition accuracy, yet simultaneously compete for limited radio resources. Moreover, the dynamic nature of practical environments further complicates this interplay due to the presence of moving interference sources and time-varying number of cellular users sharing the available bandwidth. Therefore, it is of paramount importance to jointly optimize sensing and communication resource allocation among devices and across time slots, while meticulously accounting for the impacts of dynamic environment to maximize recognition accuracy. In this paper, we propose a task-oriented integrated sensing and communication (ISAC) system for multidevice cooperative wireless motion recognition in dynamic environments. Specifically, we formulate a joint sensing and communication resource allocation problem to maximize recognition accuracy, represented by a discriminant gain metric that explicitly accounts for both sensing quality and communication constraints. Since this problem is a fractional program, we transform the original sum-of-ratios objective function into an equivalently subtractive form that facilities the development of a two-step iterative offline optimization (TSIO) algorithm to achieve the benchmark performance. Furthermore, to effectively cope with dynamic environmental influences, we further design a multi-agent reinforcement learning (MARL)-based online optimization (MRLO) scheme, which predicts environmental conditions at the subsequent time slot and adaptively optimizes resource allocation. Extensive numerical results illustrate that the proposed algorithm significantly enhances the recognition accuracy with dynamic environment influences, compared to existing benchmark algorithms. It is also observed from results that the sensing performance primarily drives recognition accuracy when energy is limited, whereas communication performance becomes the dominant factor under bandwidth constraints.