1. 9.5 TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
   • Authors: Ruijie Zheng, Xiyao Wang, Yanchao Sun, Shuang Ma, Jieyu Zhao, Huazhe Xu, Hal Daumé III, Furong Huang
   • The paper introduces a novel approach applying temporal contrastive learning for state and action representation learning in reinforcement learning, making significant impacts on both online and offline reinforcement learning scenarios.
2. 9.4 Active Coverage for PAC Reinforcement Learning
   • Authors: Aymen Al-Marjani, Andrea Tirinzoni, Emilie Kaufmann
   • Particularly intriguing is the notion of “active coverage” that allows online exploration-based learning. Through a game-theoretic algorithm, the authors allow for far more effective exploration of the environment in a reinforcement learning context.
3. 9.2 Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills
   • Authors: Ben-ya Halevy, Yehudit Aperstein, Dotan Di Castro
   • This paper presents an innovative framework for planning over offline skills to solve complex tasks in real-world environments. Its potential to enhance the application of reinforcement learning in various practical scenarios gives it a potential for impact.
4. 8.9 Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
   • Authors: Massimiliano Patacchiola, Mingfei Sun, Katja Hofmann, Richard E. Turner
   • The research interestingly provides insights on few-shot imitation learning, showing the effectiveness of a fine-tuning approach and potentially impacting future studies on control problems.
5. 8.7 Correcting discount-factor mismatch in on-policy policy gradient methods
   • Authors: Fengdi Che, Gautham Vasan, A. Rupam Mahmood
   • Reinforcement learning can benefit from the solution presented in this paper which corrects the discount factor, potentially ensuring more stable and efficient learning algorithms.