A new interpretable experiential learning model based on state history and global feedback is presented.
It is capable of learning a behavioral model represented by a transition graph between sets of states, with transitions attributed with utility and evidence count.
This model is expected to be suitable for solving reinforcement learning problem in resource-constrained environments.
The model was thoroughly evaluated on the OpenAI Gym Atari Breakout benchmark, demonstrating performance comparable to some known neural network-based solutions.