Learning Latent and Changing Dynamics in Real Non-Stationary Environments

Model-based reinforcement learning (RL) aims to learn the underlying dynamics of a given environment. The success of most existing works is built on the critical assumption that the dynamic is fixed, which is unrealistic in many open-world scenarios, such as drone delivery and online chatting, where agents may need to deal with environments with unpredictable changing dynamics (hereafter, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real non-stationary environment</i>). Therefore, learning changing dynamics in a real non-stationary environment offers both significant benefits and challenges. This paper proposes a new model-based reinforcement learning algorithm that proactively and dynamically detects possible changes and Learns these Latent and Changing Dynamics (LLCD) in a latent Markovian space for real non-stationary environments. To ensure the Markovian property of the RL model and improve computational efficiency, we employ a latent space model to learn the environment’s transition dynamics. Furthermore, we perform online change detection in the latent space to promptly identify change points in non-stationary environments. Then, we utilize the detected information to help the agent adapt to new conditions. Experiments indicate that the rewards of the proposed algorithm accumulate for the most rapid adaptions to environmental change, among other benefits. This work has a strong potential to enhance environmentally suitable model-based reinforcement learning capabilities.