1. 9.2 Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
   • Authors: Jing Tan, Ramin Khalili, Holger Karl
   • Reason: The paper addresses a highly relevant challenge in the multi-objective optimization using MARL in dynamic environments like ITS, which is a significant topic for the future of autonomous systems and smart cities. The mentioned modularized and asynchronous online training method and empirical results showcasing superior adaptation and performance suggest a strong potential for influence in the domain of distributed RL.
2. 9.0 Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
   • Authors: Rui Liu, Erfaun Noorani, Pratap Tokekar, John S. Baras
   • Reason: Focused on improving iteration complexity in reinforcement learning, which is a key aspect for RL’s applicability to real-world problems. The integration of risk sensitivity into the REINFORCE algorithm and theory-backed iteration complexity improvements, along with simulation results demonstrating quicker convergence, suggest this work could influence the development of more efficient and robust RL algorithms.
3. 8.7 SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
   • Authors: Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton
   • Reason: The paper introduces SINDy-RL, combining sparse identification of nonlinear dynamics with DRL, which offers interpretability and potentially lowers the interaction cost with the environment. The promise of comparable performance with state-of-the-art DRL while maintaining interpretability positions this work to be influential in the push towards more understandable and data-efficient RL methods.
4. 8.5 One-Shot Averaging for Distributed TD($\lambda$) Under Markov Sampling
   • Authors: Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky
   • Reason: The paper provides an improvement in communication efficiency for distributed RL with a novel “one-shot averaging” approach. Given the increasing interest in distributed machine learning methods for reducing learning times and the practical importance of communication efficiency, this work could have a significant impact on the field of distributed RL.
5. 8.3 A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
   • Authors: Nawazish Ali, Abdul Wahid, Rachael Shaw, Karl Mason
   • Reason: This paper applies Q-learning to a niche but impactful real-world problem of energy management in dairy farming, showing practical applications of RL in renewable energy integration. While more specialized, the distinctive application area and the reported improvement in electricity cost and peak demand reduction suggest this paper could influence the niche of RL applications in sustainable agriculture and renewable energy sectors.