The field of reinforcement learning is moving towards a better understanding of the theoretical foundations of policy gradient methods. Recent research has focused on analyzing the impact of distribution mismatch on these methods and has shown that they can remain globally optimal under certain conditions. Additionally, there is a growing recognition of the importance of considering implementation inconsistencies in deep reinforcement learning algorithms, which can significantly affect their performance. Furthermore, researchers are exploring new approaches to domain randomization and linear quadratic regulation, providing new insights into the convergence of policy gradient methods. Noteworthy papers include:

• Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch, which offers new insights into the robustness of policy gradient methods.
• On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementations, which highlights the significant discrepancies between different implementations of the same algorithm.
• Policy Gradient for LQR with Domain Randomization, which provides the first convergence analysis of policy gradient methods for domain-randomized linear quadratic regulation.
• Ordering-based Conditions for Global Convergence of Policy Gradient Methods, which establishes new general results for the global convergence of policy gradient methods under linear function approximation.