We apply RL to basin-hopping CSP by adapting and implementing the reinforcement learning algorithm REINFORCE , which is based on "policy gradients". With this approach, the policy is parametrised, and the parameters of the policy are improved over time using gradientbased approaches, where the gradients are based on recent experience of the current policy so that actions that led to desirable outcomes are likely to be used again in similar situations. Intuitively, the RL algorithm leverages its past experience to select good actions when similar situations are encountered again as the run proceeds. We refer to this variation of REINFORCE in the CSP context as RL-CSP and illustrate it in Fig. .