The field of Markov decision processes is moving towards addressing challenges in non-stationary and partially observable environments. Researchers are exploring innovative structures and algorithms to facilitate decision-making in complex, time-varying contexts. Notable developments include the exploitation of underlying Markov chains to enable convergence of traditional algorithms and the use of annealed importance resampling to improve observation adaptation in partially observable Markov decision processes.

Recent work has also focused on applying these advances to real-world problems, such as communication networks and real-time tracking systems. The development of effective control policies for these systems has shown significant promise in improving performance and minimizing costs.

Some noteworthy papers in this area include:

• A study on reinforcement learning in switching non-stationary Markov decision processes, which introduces a novel structure to facilitate algorithms and convergence analysis.
• A proposal for observation adaptation via annealed importance resampling for partially observable Markov decision processes, demonstrating superior performance compared to state-of-the-art methods.