🤖 AI Summary
This work addresses the challenge of explainable reinforcement learning in resource-constrained environments by proposing an experience-based learning model grounded in state-transition graphs. The model explicitly constructs a graph encoding both utility values and evidence counts, integrating a global feedback mechanism to enable transparent policy modeling and interpretable decision-making. By embedding explainability directly into the learning process, the approach maintains low computational overhead while achieving performance on the OpenAI Gym Atari Breakout benchmark comparable to that of certain neural network–based methods, thereby demonstrating its effectiveness and practicality in settings with limited computational resources.
📝 Abstract
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.