9.5 Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G Coordination for Multi-Stakeholder Benefits
- Authors: Yubao Zhang, Xin Chen, Yi Gu, Zhicheng Li, Wu Kai
- Reason: This paper combines deep RL and V2G techniques for multi-stakeholder benefits. With the growing prevalence of electric vehicles, this research could set a new standard in the industry. The authors also have significant authority in their field adding to the potential influence of the paper.
9.2 Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
- Authors: Giorgio Franceschelli, Mirco Musolesi
- Reason: This survey paper covers a key aspect of machine learning - Reinforcement Learning (RL) for generative AI. It discusses opportunities and challenges, making it a useful reference for researchers in this field.
9.1 Pixel to policy: DQN Encoders for within & cross-game reinforcement learning
- Authors: Ashrya Agrawal, Priyanshi Shah, Sourabh Prakash
- Reason: This paper investigates transfer learning within the domain of reinforcement learning, a research area with high potential. The high performance of their model could influence further research in this domain.
8.9 Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias
- Authors: Itay Itzhak, Gabriel Stanovsky, Nir Rosenfeld, Yonatan Belinkov
- Reason: This paper examines the biases in instruction-tuned language models, an important topic due to the increasing use of these models in society. Addressing these issues could have a far-reaching impact.
8.8 Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
- Authors: Nathan Haut, Wolfgang Banzhaf, Bill Punch
- Reason: The paper is important because it explores active learning in genetic programming, specifically focusing on efficient data collection for symbolic regression. This could have big implications for the field as it could optimize the process of data collection.