  We present a novel machine learning framework tailored to detect massive
black hole binaries observed by spaceborne gravitational wave detectors like
the Laser Interferometer Space Antenna (LISA) and predict their future merger
times. The detection is performed via convolutional neural networks that
analyze time-evolving Time-Delay Interferometry (TDI) spectrograms and utilize
variations in signal power to trigger alerts. The prediction of future merger
times is accomplished with reinforcement learning. Here, the proposed algorithm
dynamically refines time-to-merger predictions by assimilating new data as it
becomes available. Deep Q-learning serves as the core technique of the
approach, utilizing a neural network to estimate Q-values throughout the
observational state space. To enhance robust learning in a noisy environment,
we integrate an actor-critic mechanism that segregates action proposals from
their evaluation, thus harnessing the advantages of policy-based and
value-based learning paradigms. We leverage merger estimation obtained via
template matching with truncated waveforms to generate rewards for the
reinforcement learning agent. These estimations come with uncertainties, which
magnify as the merger event stretches further into the future. The
reinforcement learning setup is shown to adapt to these uncertainties by
employing a policy fine-tuning approach, ensuring the reliability of
predictions despite the varying degrees of template-matching precision. The
algorithm denotes a first step toward a low-latency model that provides early
warnings for impending transient phenomena. By delivering timely and accurate
forecasts of merger events, the framework supports the coordination of
gravitational wave observations with accompanied electromagnetic counterparts,
thus enhancing the prospects of multi-messenger astronomy. We use the LISA
Spritz data challenge for validation.
