To meet time constraints, a CBR system must control the time spent searching in the case base for a solution. In this paper, we presents the results of a case study comparing the p...
In the paper, we investigate the use of reinforcement learning in CBR for estimating and managing a legacy case base for playing the game of Tetris. Each case corresponds to a loc...
Modern embedded compute platforms increasingly contain both microprocessors and field-programmable gate arrays (FPGAs). The FPGAs may implement accelerators or other circuits to s...
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning...
Most conventional Policy Gradient Reinforcement Learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the pol...