  Neural ordinary differential equations (ODEs) are widely recognized as the
standard for modeling physical mechanisms, which help to perform approximate
inference in unknown physical or biological environments. In partially
observable (PO) environments, how to infer unseen information from raw
observations puzzled the agents. By using a recurrent policy with a compact
context, context-based reinforcement learning provides a flexible way to
extract unobservable information from historical transitions. To help the agent
extract more dynamics-related information, we present a novel ODE-based
recurrent model combines with model-free reinforcement learning (RL) framework
to solve partially observable Markov decision processes (POMDPs). We
experimentally demonstrate the efficacy of our methods across various PO
continuous control and meta-RL tasks. Furthermore, our experiments illustrate
that our method is robust against irregular observations, owing to the ability
of ODEs to model irregularly-sampled time series.
