1. 9.5 Landmark Guided Active Exploration with Stable Low-level Policy Learning
   • Authors: Fei Cui, Jiaojiao Fang, Mengke Yang, Guizhong Liu
   • Reason: Novel approach for efficient training and exploration in the domain of Goal-conditioned hierarchical reinforcement learning making active utilization of goal-conditioned value function, with promising experimental results. Considering the citation records of these authors, this paper has a lot of potential to influence the field of reinforcement learning.
2. 9.1 $λ$-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces
   • Authors: Claas A Voelcker, Arash Ahmadian, Romina Abachi, Igor Gilitschenski, Amir-massoud Farahmand
   • Reason: The paper gives theoretical backing to decision-aware reinforcement learning models and proposes a valuable framework for continuous state-spaces which can have extensive applications. The authors also pose significant reputation in the field.
3. 8.7 Probabilistic Constraint for Safety-Critical Reinforcement Learning
   • Authors: Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain
   • Reason: This paper tackles the crucial aspect of safety in reinforcement learning, providing several theoretical contributions apart from a novel approach that could change the way we achieve safety in RL applications.
4. 8.4 Learning Environment Models with Continuous Stochastic Dynamics
   • Authors: Martin Tappler, Edi Muškardin, Bernhard K. Aichernig, Bettina Könighofer
   • Reason: The paper presents a scalable approach to learning complex environments helping to enhance control tasks functionality using deep reinforcement learning. The proposed method has shown commendable results on popular benchmarking environments.
5. 8.0 Optimal Execution Using Reinforcement Learning
   • Authors: Cong Zheng, Jiafa He, Can Yang
   • Reason: The paper works on the optimal order execution problem with respect to cryptocurrency exchanges showing promising results but due to the less comprehensive nature and limited scope, it gets a lower score.