Parul Goyal
Papers
1
Total Citations
20
H-Index
1
About
Parul Goyal is a researcher whose work bridges the frontiers of artificial intelligence and control systems, with a particular focus on evolutionary reinforcement learning (ERL). Her most-cited paper, "Application of Evolutionary Reinforcement Learning (ERL) Approach in Control Domain: A Review" (2018), has garnered 20 citations, establishing her as a thoughtful synthesizer of emerging techniques. In this review, Goyal systematically maps how ERL—a hybrid that combines the exploration strengths of evolutionary algorithms with the decision-making power of reinforcement learning—can be applied to complex control tasks, from robotics to autonomous systems. Her contribution lies in clarifying the synergies between these two paradigms, offering a structured taxonomy of approaches and identifying key challenges like sample efficiency and policy transfer. This work has provided a foundational reference for researchers seeking to leverage ERL in real-world control applications. While her citation count reflects the niche but growing interest in this area, Goyal’s review stands out for its clarity and foresight, helping to shape how the field understands the integration of evolution and learning. Her research is particularly valuable for students and engineers exploring adaptive control solutions, as it demystifies a complex, interdisciplinary domain.
Research Focus
Key Achievements
Top Papers
- 1