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We propose a generic reward shaping approach for improving the rate ofconvergence in reinforcement learning (RL), called Self Improvement BasedREwards, or SIBRE. The approach is designed for use in conjunction with anyexisting RL algorithm, and consists of rewarding improvement over the agent'sown past performance. We prove that SIBRE converges in expectation under thesame conditions as the original RL algorithm. The reshaped rewards helpdiscriminate between policies when the original rewards are weaklydiscriminated or sparse. Experiments on several well-known benchmarkenvironments with different RL algorithms show that SIBRE converges to theoptimal policy faster and more stably. We also perform sensitivity analysiswith respect to hyper-parameters, in comparison with baseline RL algorithms.

SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning