Taxonomies can serve as a vital foundation for several downstream tasks such as information retrieval and question answering, yet manual construction limits coverage and full potential. Automatic taxonomy induction, particularly using deep Reinforcement Learning (RL), is underexplored in Natural Language Processing (NLP). To address this gap, we present TaxoCritic, a novel approach that leverages deep multi-critic RL agents for taxonomy induction while incorporating credit assignment mechanisms. Our system uniquely assesses different sub-actions within the induction process, providing a granular analysis that aids in the precise attribution of credit and blame. We evaluate the effectiveness of multi-critic algorithms in experiments regarding both accuracy and robustness performance in edge identification. By providing a detailed comparison with state-of-the-art models and highlighting the strengths and limitations of our method, we aim to contribute to the ongoing