1. 9.7 IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse
   • Authors: Siyuan Li, Hao Li, Jin Zhang, Zhen Wang, Peng Liu, Chongjie Zhang
   • Reason: The paper addresses a fundamental challenge in reinforcement learning – transferring knowledge from prior policies to new tasks. The authors propose a novel method that circumvents the need for additional components used by prior methods. As a result, it significantly enhances transfer effectiveness and offers improvement in final performance and knowledge transferability in continual learning scenarios.
2. 9.3 Omega-Regular Reward Machines
   • Authors: Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, Dominik Wojtczak
   • Reason: The paper introduces omega-regular reward machines, which is a significant contribution towards enhancing the reward mechanism in reinforcement learning. They provide a model-free RL algorithm to compute epsilon-optimal strategies against their reward machines, potentially improving training results for agents.
3. 9.2 Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks
   • Authors: Amr Abdelraouf, Rohit Gupta, Kyungtae Han
   • Reason: This work presents a novel approach for personalized vehicle trajectory prediction, which is an important problem in AI-related applications. Their methodology leverages transfer learning and demonstrates enhanced prediction accuracy, which can find practical applications in autonomous vehicles.
4. 9.1 Dyadic Reinforcement Learning
   • Authors: Shuangning Li, Lluis Salvat Niell, Sung Won Choi, Inbal Nahum-Shani, Guy Shani, Susan Murphy
   • Reason: This paper proposes a novel concept of dyadic reinforcement learning, which considers interactions happening in social context, extending the traditional reinforcement learning framework. This new concept can benefit mobile health applications, where such dyads of patient-carer interactions can be critical for improving health outcomes.
5. 9.0 Variations on the Reinforcement Learning performance of Blackjack
   • Authors: Avish Buramdoyal, Tim Gebbie
   • Reason: The paper investigates reinforcement learning in the context of a card-based game - Blackjack. The novelty of their work lies in using a q-learning solution for optimal play and investigating the rate of learning convergence. It brings a new perspective towards understanding the behavior of reinforcement learning agents amid environment variations.