Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning
This addresses the detachment issue in intrinsic motivation for multi-agent systems, but appears incremental as it combines existing methods.
The paper tackles the sample-efficiency problem in sparse-reward multi-agent reinforcement learning by proposing I-Go-Explore, a method that combines intrinsic curiosity with the Go-Explore framework to alleviate the detachment problem, though no concrete performance numbers are provided.
Sparsity of rewards while applying a deep reinforcement learning method negatively affects its sample-efficiency. A viable solution to deal with the sparsity of rewards is to learn via intrinsic motivation which advocates for adding an intrinsic reward to the reward function to encourage the agent to explore the environment and expand the sample space. Though intrinsic motivation methods are widely used to improve data-efficient learning in the reinforcement learning model, they also suffer from the so-called detachment problem. In this article, we discuss the limitations of intrinsic curiosity module in sparse-reward multi-agent reinforcement learning and propose a method called I-Go-Explore that combines the intrinsic curiosity module with the Go-Explore framework to alleviate the detachment problem.