Self-Model-Free Learning Versus Learning With External Rewards in Information Constrained Environments

Abstract

In this article, we provide a model-free reinforcement learning (RL) framework that relies on internal reinforcement signals, called self-model-free RL, for learning agents that experience loss of the reinforcement signals in the form of packet drops and/or jamming attacks by malicious agents. The framework embeds a correcting mechanism in the form of a goal network to compensate for information loss and produce optimal and stabilizing policies. It also provides a trade-off scheme that reconstructs the reward using a goal network whenever the reinforcement signals are lost but utilizes true reinforcement signals when they are available. The stability of the equilibrium point is guaranteed despite fractional information loss in the reinforcement signals. Finally, simulation results validate the efficacy of the proposed work.