Predictor based on Temporal Difference learning (TD-learning)


Check high level predictor docs for predictor basics.

See Wikipedia for a brief intro to Temporal Difference learning.

Also check Monte Carlo Tree Search for additional details..

Resolving software stacks with using TD-learning.

The figures below show the resolution process which is guided using TD-learning.

The first figure shows the annealing schedule that is balancing exploration and exploitation. The acceptance probability of a neighbour candidate starts to significantly decrease approximately in iteration 70000. This collerates with product creation cadence as exploitation phase is starting based on the resolution progress.

Progress made during resolution guided by a TD-learning based predictor.

Scores of software stacks reflect exploration and exploitation phase as well, as can be seen in the figure below. The green dots reflecting score of the states resolved are spread until approx. 70000th iteration of resolver when the exploration phase slowly ends. The average score of software stacks produced during the exploitation phase is higher. The best software stack candidate is found in approx. 90000th resolver iteration in the exploitation phase.

Score of resolved software stacks as produced by the resolution pipeline and score of the best software stack found.

Number of software stacks in beam grows until 70000th iteration after which it starts to grow slower considering the exploitation phase start.

States captured in the resolver's beam.


The exploration and exploitation phase is balanced using an annealing schedule. The balance factor can be parametrized using temperature_coefficient parameter that can be supplied to the predictor. see reinforcement learning intro section for more info.

n-step TD-learning

The implementation of predictor accepts a parameter “step” which turns on n-step TD-learning behaviour. By default, the step parameter default to 1.

Tracing parameter in TD-learning

Another parameter called trace can turn off propagation of the reward signal to other resolved dependencies. It’s not recommended to turn this parameter on if step is not equal to 1.

The tracing can be turned off if the knowledge base does not provide causality in steps (package X is scored in a better way if Y is part of the software stack). In such cases, the resolution process can be faster.