# Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer
## 5 Conclusions  
In our work, we explore a novel application of reinforcement learning in the domain of Natural Language Processing (NLP), specifically focusing on the Text Translation Task. Drawing inspiration from the original work on the Dual-Transformer (DT), we devise a unique approach to data segmentation and encoding, tailoring it to meet the requirements of bipartite training for the Transformer model in the context of offline reinforcement learning. The designed ATT model structure incorporates a comprehensive set of Train and Inference processes, ensuring consistent data distribution across different stages of the task for effective training. By utilizing an input method that considers both the environment and target rewards and predicts action values, our model aligns with the reinforcement learning objective of identifying optimal actions and generally outperforms DT in various tasks.  
Given that the combination of language modeling and reinforcement learning is still in its early stages, and NLP is rapidly evolving with the emergence of more efficient models, the potential for RL-NLP integration remains vast. In light of our model's future research direction, we identify key aspects for consideration:  
* **Target Reward Setting:** In both ATT and DT, we adopt the supervised learning task training approach, requiring the calculation of a target reward value as the trajectory label for offline dataset training. Fluctuations in experimental results may be attributed to the accurate setting of reward values. Future research should explore methods to precisely set reward values or update them dynamically during training.
* **Efficient Language Model Training:** The language model, serving as the foundational training architecture, demands a substantial amount of datasets for effective training. While reinforcement learning can generate trajectory data through interaction, this method is not applicable to online reinforcement learning due to efficiency concerns. Investigating how to train the language model in an online interactive manner becomes a focal point for future work, expanding the model's usability.
* **Exploration of Different NLP Models:** The field of NLP boasts numerous powerful models, each with distinct characteristics and adaptive capabilities. Future endeavors  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environment** & **ATT** & **DT** & **CQL** \\ \hline Maze2D-umaze & 42.2 & 31.0 & **94.7** \\ Maze2D-medium & 13.7 & 8.2 & **41.8** \\ Maze2D-large & 10.2 & 2.3 & **49.6** \\ \hline \hline \end{tabular} The bold numbers in each row represent the best performance in the corresponding environment for that row.  
\end{table}
Table 3: Comparison of ATT with other offline reinforcement learning algorithms in Maze2D.  
can involve experimenting with other high-performing NLP models, aligning them with various reinforcement learning tasks, and further exploring the potential of combining these technologies.  
Methodology, software, validation and writing--original draft preparation, J.L.; writing--review and editing, N.X. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (Grant No. 2022 YFB3104600), and by Chengdu Science and Technology Project (2019-YF08-00285-GX), and by the National Natural Science Foundation of China under Grant NO. 61976156, and by Intelligent Terminal Key Laboratory of SiChuan Province under Grant SCITLAB-30005.  
The dataset used in this study is the publicly available dataset D4 RL, which can be found at the following website: [[https://github.com/Farama-Foundation/D4](https://github.com/Farama-Foundation/D4) RL]([https://github.com/Farama-Foundation/D4](https://github.com/Farama-Foundation/D4) RL), accessed on 7 January 2024.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.