The expected outcomes of this research include: 1) Proposing a policy search algorithm suitable for non-Markovian decision processes, providing a more efficient solution for complex decision-making problems; 2) Validating the advantages of this algorithm in capturing historical dependencies and improving decision-making efficiency, offering a basis for practical applications; 3) Identifying the limitations of the algorithm and proposing optimization directions, promoting further development in related fields. These outcomes will help improve the research level of non-Markovian decision processes, advance the application of AI systems in complex tasks, and provide experimental data and application scenarios for the further optimization of OpenAI models.
Research
Exploring non-Markovian decision processes through theoretical and experimental validation.
Innovative Research Solutions
Exploring non-Markovian decision processes through theoretical analysis and experimental validation for enhanced policy search algorithms.