Salganicoff, Marcos2023-05-222023-05-221993-12-012006-09-13https://repository.upenn.edu/handle/20.500.14332/37615We describe a density-adaptive reinforcement learning and a density-adaptive forgetting algorithm. This learning algorithm uses hybrid k-D/2k-trees to allow for a variable resolution partitioning and labelling of the input space. The density adaptive forgetting algorithm deletes observations from the learning set depending on whether subsequent evidence is available in a local region of the parameter space. The algorithms are demonstrated in a simulation for learning feasible robotic grasp approach directions and orientations and then adapting to subsequent mechanical failures in the gripper.Report