1. 9.5 Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games
   • Authors: Zelai Xu, Yancheng Liang, Chao Yu, Yu Wang, Yi Wu
   • Reason: This paper provides a novel algorithm for competitive games that includes a new framework for Nash equilibrium learning, which holds significant implications in multi-agent reinforcement settings for mixed cooperative-competitive games.
2. 9.4 RUSOpt: Robotic UltraSound Probe Normalization with Bayesian Optimization for In-plane and Out-plane Scanning
   • Authors: Deepak Raina, Abhishek Mathur, Richard M. Voyles, Juan Wachs, SH Chandrashekhara, Subir Kumar Saha
   • Reason: This paper introduces a method to improve the orientation of robotic ultrasound probes to enhance the quality of image acquisition, which is highly significant in medical robotics field.
3. 9.3 Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms
   • Authors: Akifumi Wachi, Wataru Hashimoto, Xun Shen, Kazumune Hashimoto
   • Reason: This paper focuses on a critical issue in real-world RL applications - safe exploration. It addresses this through a generalized formulation and proposes a meta-algorithm for safe exploration while optimizing the policy, supported by test cases and comparisons to existing algorithms. This paper could have a significant impact considering the wide application of RL and the importance of safety.
4. 9.3 LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework
   • Authors: Woojun Kim, Jeonghye Kim, Youngchul Sung
   • Reason: This paper offers an innovative framework for reinforcement learning that can adaptively select the most effective exploration strategy, which is particularly useful for tackling the exploration-exploitation dilemma in RL.
5. 9.2 How the level sampling process impacts zero-shot generalisation in deep reinforcement learning
   • Authors: Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht
   • Reason: This paper involves a detailed investigation into how level sampling influences the generalization ability of RL agents, addressing a key issue in the operability of RL models.
6. 9.1 Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
   • Authors: Yihang Yao, Zuxin Liu, Zhepeng Cen, Jiacheng Zhu, Wenhao Yu, Tingnan Zhang, Ding Zhao
   • Reason: Introducing an approach to achieve versatile safe reinforcement learning, this paper demonstrates a significant contribution improving the safety and performance of RL agents in varying constraint thresholds data-efficiently.
7. 8.9 Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
   • Authors: Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, Masoud Jalayer, Maryam Ahang, Ardeshir Shojaeinasab, Homayoun Najjaran
   • Reason: The paper covers an important combinatorial problem - machine scheduling, which is crucial in manufacturing. Applying DRL to this problem has significant potential benefits and this comprehensive review, comparing DRL-based approaches and discussing potential future directions, might be highly influential for researchers and practitioners in this field.
8. 8.7 A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback
   • Authors: Jianghong Zhou, Joyce C. Ho, Chen Lin, Eugene Agichtein
   • Reason: The authors propose a deep Q-learning approach to interactive search, considering sentence-level feedback. As searches often fail to capture user intent accurately, this paper’s focus on fine-grained feedback and natural language processing could have a considerable influence on interactive search systems and RL applications.
9. 8.5 Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
   • Authors: Sara Klein, Simon Weissmann, Leif Döring
   • Reason: The paper introduces a dynamic policy gradient for Markov Decision Processes, which could advance the field by offering an alternative to the commonly used stationary policies and improving convergence bounds.
10. 8.1 Neural architecture impact on identifying temporally extended Reinforcement Learning tasks
    • Authors: Victor Vadakechirayath George
    • Reason: The exploration of different attention-based architectures for reinforcement learning on the Atari-2600 game suite could spark further research and developments in this area.