  We present a reinforcement learning toolkit for experiments with guiding
automated theorem proving in the connection calculus. The core of the toolkit
is a compact and easy to extend Prolog-based automated theorem prover called
plCoP. plCoP builds on the leanCoP Prolog implementation and adds
learning-guided Monte-Carlo Tree Search as done in the rlCoP system. Other
components include a Python interface to plCoP and machine learners, and an
external proof checker that verifies the validity of plCoP proofs. The toolkit
is evaluated on two benchmarks and we demonstrate its extendability by two
additions: (1) guidance is extended to reduction steps and (2) the standard
leanCoP calculus is extended with rewrite steps and their learned guidance. We
argue that the Prolog setting is suitable for combining statistical and
symbolic learning methods. The complete toolkit is publicly released.
