The architecture of a modular behavioral agent (MBA) with learning ability for hardware realization is proposed to implement a multilevel behavioral robot such that it can autonomously complete a complex task. The architecture is composed of similar modules, primitive actions, and composition behaviors. These modules, which are derived from a basic template, are capable of learning and cooperating to cope with a variety of tasks. The infrastructure of a template embeds a reinforcement learning mechanism with an adaptable receptive module (ARM)-based critic-actor model. Each template executes one specified behavior and also cooperates with other templates to form a more dexterous composed behavior. In other words, the composed behavior is constructed by several primitives with similar modular architecture. The learning and cooperation abilities in the modules are based on a reinforcement learning technique, which is based on a critic-actor model. The proposed architecture is implemented in a field-programmable gate array (FPGA) chip with a CPU core such that the computing device can fully utilize the merits of parallel processing of neural networks in the ARM scheme. The study is demonstrated on a mobile robot for goal-seeking, cruise , and safetyensurance tasks in an unstructured environment with obstacles such as walls and blocks. The results show that this robot with the modular architecture can perform well in unstructured environments.