While recent advances in language modeling have resulted in powerful\ngeneration models, their generation style remains implicitly dependent on the\ntraining data and can not emulate a specific target style. Leveraging the\ngenerative capabilities of a transformer-based language models, we present an\napproach to induce certain target-author attributes by incorporating continuous\nmulti-dimensional lexical preferences of an author into generative language\nmodels. We introduce rewarding strategies in a reinforcement learning framework\nthat encourages the use of words across multiple categorical dimensions, to\nvarying extents. Our experiments demonstrate that the proposed approach can\ngenerate text that distinctively aligns with a given target author's lexical\nstyle. We conduct quantitative and qualitative comparisons with competitive and\nrelevant baselines to illustrate the benefits of the proposed approach.\n