1. 9.7 Maneuver Decision-Making Through Automatic Curriculum Reinforcement Learning Without Handcrafted Reward functions
   • Authors: Zhang Hong-Peng
   • Reason: A unique approach to reinforcement learning for decision-making in unmanned combat aerial vehicles using automatic curriculum learning strategies. The author is a recognised authority in this area and the ablation study findings provide new insights for reinforcement learning.
2. 9.6 Transformers in Reinforcement Learning: A Survey
   • Authors: Pranav Agarwal, Aamer Abdul Rahman, Pierre-Luc St-Charles, Simon J.D. Prince, Samira Ebrahimi Kahou
   • Reason: This paper provides a comprehensive review of the use of Transformers in reinforcement learning. It presents the current challenges and discusses how Transformers can be adapted for several applications. The authors and their affiliations suggest extensive experience and significant contribution in the field of AI, enhancing the paper’s credibility and potential impact.
3. 9.5 Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior
   • Authors: Kai Cui, Sascha Hauck, Christian Fabian, Heinz Koeppl
   • Reason: The paper presents novel models for decentralized partially observable Mean Field Control (MFC). The research is significant because it addresses key challenges in multi-agent reinforcement learning. The in-depth theoretical results along with practical application make this paper influential.
4. 9.4 Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing
   • Authors: Sanae Amani, Khushbu Pahwa, Vladimir Braverman, Lin F. Yang
   • Reason: The paper represents significant work in the area of multi-task reinforcement learning. The authors introduce a new algorithm and empirically demonstrate its effectiveness, which can greatly contribute to the progress in the distributed reinforcement learning field.
5. 9.3 DSSE: a drone swarm search environment
   • Authors: Manuel Castanares, Luis F. S. Carrete, Enrico F. Damiani, Leonardo D. M. de Abreu, José Fernando B. Brancalion, Fabrício J. Barth
   • Reason: The paper proposes a Drone Swarm Search environment based on PettingZoo that uses multi-agent reinforcement learning algorithms. The unique approach of this project and its potential for studying reinforcement learning algorithms makes it a potential influence.
6. 9.2 Safe Reinforcement Learning for Strategic Bidding of Virtual Power Plants in Day-Ahead Markets
   • Authors: Ognjen Stanojev, Lesia Mitridati, Riccardo de Nardis di Prata, Gabriela Hug
   • Reason: This paper presents a novel safe reinforcement learning algorithm with a unique application in the energy sector. The method developed could potentially play a significant role in shifting towards more sustainable energy consumption and production.
7. 9.1 Diffusion Based Multi-Agent Adversarial Tracking
   • Authors: Sean Ye, Manisha Natarajan, Zixuan Wu, Matthew Gombolay
   • Reason: The paper introduces a new approach to target tracking with the Constrained Agent-based Diffusion for Enhanced Multi-Agent Tracking (CADENCE). The estimated trajectory predictions and the innovative use of a cross-attention-based diffusion model contribute to its potential influence.
8. 9.0 VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
   • Authors: Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li, Jiajun Wu, Li Fei-Fei
   • Reason: This work presents a comprehensive methodology for robot manipulation using language models, successfully tackling the problem of task-based manipulations and providing a promising direction for future works in robotics.
9. 9.0 Budgeting Counterfactual for Offline RL
   • Authors: Yao Liu, Pratik Chaudhari, Rasool Fakoor
   • Reason: The paper presents a unique way to budget the number of counterfactual decisions a policy can make, thereby controlling extrapolation in offline reinforcement learning. This method’s different approach to balance the risk and error of extrapolation in reinforcement learning could have potential influence.
10. 8.8 Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning
    • Authors: Rixin Wu, Ran Wang, Jie Hao, Qiang Wu, Ping Wang
    • Reason: This paper presents a transformer-based deep reinforcement learning approach for hydropower management, which is a critical issue in sustainable development. The practical application of this method and the promising experimental results can have significant implications in the energy industry.