  Maintaining the long-term exploration capability of the agent remains one of
the critical challenges in deep reinforcement learning. A representative
solution is to leverage reward shaping to provide intrinsic rewards for the
agent to encourage exploration. However, most existing methods suffer from
vanishing intrinsic rewards, which cannot provide sustainable exploration
incentives. Moreover, they rely heavily on complex models and additional memory
to record learning procedures, resulting in high computational complexity and
low robustness. To tackle this problem, entropy-based methods are proposed to
evaluate the global exploration performance, encouraging the agent to visit the
state space more equitably. However, the sample complexity of estimating the
state visitation entropy is prohibitive when handling environments with
high-dimensional observations. In this paper, we introduce a novel metric
entitled Jain's fairness index (JFI) to replace the entropy regularizer, which
solves the exploration problem from a brand new perspective. In sharp contrast
to the entropy regularizer, JFI is more computable and robust and can be easily
applied generalized into arbitrary tasks. Furthermore, we leverage a
variational auto-encoder (VAE) model to capture the life-long novelty of
states, which is combined with the global JFI score to form multimodal
intrinsic rewards. Finally, extensive simulation results demonstrate that our
multimodal reward shaping (MMRS) method can achieve higher performance than
other benchmark schemes.
