trial and error learning

beebee is directed by the peer that hold agency but it requires techniques to learn from those instructions and to generalize them when appropriate. Learning from trail and error or in NN world, reinforcement learning, i.e. provide a goal and reward. In beebee case the reward is to make a good or better prediction or simulation. Let give an example: use the BentoBoxDS tools analyze heart data after swimming on a Monday, Wednesday and Friday and compare to none swimming days? Now, beebee should be able to 'generalize' this to say, do the same but for body fat or hydration levels. beebee could even volunteer up such computations or bring a peer attention to an interesting chart if it has been granted a degree of freedom to autonomously carry out besearch within a besearch cycle.