1. 9.6 Episodic Return Decomposition by Difference of Implicitly Assigned Sub-Trajectory Reward
   • Authors: Haoxin Lin, Hongqiu Wu, Jiaji Zhang, Yihao Sun, Junyin Ye, Yang Yu
   • Reason: The paper introduces Diaster, a novel method for decomposing episodic rewards into proxy step-wise rewards, which is crucial for handling delayed rewards in RL. This approach not only is theoretically grounded but also empirically outperforms state-of-the-art methods, indicating a strong influence on future RL algorithms, especially in domains with delayed rewards.
2. 9.4 Multi-agent Reinforcement Learning: A Comprehensive Survey
   • Authors: Dom Huh, Prasant Mohapatra
   • Reason: Comprehensive surveys often serve as a crucial resource for advancing a field. This paper’s potential influence is amplified by its thorough examination of multi-agent reinforcement learning (MARL), which is a highly active area within RL.
3. 9.3 CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory Optimization
   • Authors: Elisa Alboni, Gianluigi Grandesso, Gastone Pietro Rosati Papini, Justin Carpentier, Andrea Del Prete
   • Reason: CACTO-SL is an extension of the CACTO algorithm, integrating Trajectory Optimization and Sobolev learning, which is innovative in the RL domain. Given its efficiency and potential to improve exploration policies significantly, it is likely to impact the field of continuous control and could be adopted in robotics and optimization problems, underlining its influence.
4. 9.2 Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex Programming
   • Authors: Minjae Cho, Chuangchuang Sun
   • Reason: Safety and adaptability in RL are critical for real-world applications. By exploring a novel method for constrained meta-RL, this paper may significantly impact areas such as autonomous driving and healthcare.
5. 9.1 Pareto Envelope Augmented with Reinforcement Learning
   • Authors: Paul Seurin, Koroush Seurin
   • Reason: Techniques that improve efficiency in solving multi-objective optimization problems are valuable. The practical applications demonstrated in PWR core Loading Pattern optimization suggest a strong potential influence on engineering fields.
6. 8.9 Active Reinforcement Learning for Robust Building Control
   • Authors: Doseok Jang, Larry Yan, Lucas Spangher, Costas Spanos
   • Reason: This paper confronts the brittleness of RL and proposes a method that could lead to more robust RL applications in building control, which is an area of growing interest due to energy efficiency concerns.
7. 8.9 GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation
   • Authors: Abhinav Jain, Vaibhav Unhelkar
   • Reason: GO-DICE addresses the challenge of learning policies for long-horizon tasks in offline imitation learning, suggesting a hierarchical approach that improves task completion rates in challenging robotic tasks. The method accommodates goal-conditioned learning which is valuable for retraining models, thus positioning it to be highly influential in IL and challenging control tasks.
8. 8.7 Deriving Rewards for Reinforcement Learning from Symbolic Behaviour Descriptions of Bipedal Walking
   • Authors: Daniel Harnack, Christoph Lüth, Lukas Gross, Shivesh Kumar, Frank Kirchner
   • Reason: The approach of integrating symbolic AI with RL for generating movement behaviors demonstrates an innovative cross-disciplinary methodology. Whilst it’s more specialized, applications in robotics could render it quite influential.
9. 8.7 Deep-Dispatch: A Deep Reinforcement Learning-Based Vehicle Dispatch Algorithm for Advanced Air Mobility
   • Authors: Elaheh Sabziyan Varnousfaderani, Syed A. M. Shihab, Esrat F. Dulia
   • Reason: This paper tackles an emerging and practically relevant domain of eVTOL aircraft dispatch, where operational constraints are unique. The novel deep RL algorithms developed here show promise in reducing computational expenses and improving policy performance, pointing to potential impact on the burgeoning field of advanced air mobility.
10. 8.6 Colored Noise in PPO: Improved Exploration and Performance Through Correlated Action Sampling
    • Authors: Jakob Hollenstein, Georg Martius, Justus Piater
    • Reason: Introduces a significant improvement to a popular RL algorithm (PPO), which might have a widespread impact considering the popularity of the method.
11. 8.5 Learning to Act without Actions
    • Authors: Dominik Schmidt, Minqi Jiang
    • Reason: The approach of pre-training RL policies using action-free demonstrations is groundbreaking, addressing the scarcity of labeled behavioral data for RL. The potential for rapid fine-tuning to expert-level performance could significantly influence how RL models are trained, particularly in leveraging the abundance of web data.
12. 8.3 Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomes
    • Authors: Yotam Amitai, Yael Septon, Ofra Amir
    • Reason: Accepted to a major conference (AAAI 2024) and deals with a growing area of interest in RL, explainability, which is crucial for real-world applications.
13. 8.1 Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis
    • Authors: Rohan Mitta, Hosein Hasanbeig, Jun Wang, Daniel Kroening, Yiannis Kantaros, Alessandro Abate
    • Reason: Addresses the vital issue of safety in RL during training, a key concern for deploying RL in real-world scenarios, particularly around humans.
14. 7.8 Challenges for Reinforcement Learning in Quantum Computing
    • Authors: Philipp Altmann, Adelina Bärligea, Jonas Stein, Michael Kölle, Thomas Gabor, Thomy Phan, Claudia Linnhoff-Popien
    • Reason: Tackles an emerging intersection between RL and quantum computing, which could be influential as quantum technologies mature.
15. 7.5 Monte Carlo Tree Search in the Presence of Transition Uncertainty
    • Authors: Farnaz Kohankhaki, Kiarash Aghakasiri, Hongming Zhang, Ting-Han Wei, Chao Gao, Martin Müller
    • Reason: Monte Carlo Tree Search is widely used in various domains, and improving its robustness to model imperfections could enhance its application to more realistic settings.