The field of reinforcement learning is moving towards more flexible and generalizable methods for behavior alignment and credit assignment. Researchers are exploring alternative approaches to reward function design, such as recursive reward aggregation and value-aware eigenoptions, to improve the effectiveness of reinforcement learning algorithms. Another key area of focus is the development of more advanced techniques for multi-agent reinforcement learning, including methods for achieving monotonic improvement and fine-grained temporal credit assignment. Additionally, there is a growing interest in understanding the limitations and potential misalignments of reward functions, as well as the importance of representation learning in reinforcement learning. Notable papers in this area include:

• Recursive Reward Aggregation, which proposes a novel approach for flexible behavior alignment by introducing an algebraic perspective on Markov decision processes.
• ToMacVF, which achieves fine-grained temporal credit assignment for macro-action contributions in asynchronous multi-agent reinforcement learning.
• Spectral Bellman Method, which introduces a novel framework for learning state-action features that inherently capture the Bellman-aligned covariance structure.