Never Explore Repeatedly in Multi-Agent Reinforcement Learning

Understand

In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration.

• While the computation of many intrinsic rewards relies on estimating variational posteriors using neural network approximators, a notable challenge has surfaced due to the limited expressive capability of these neural statistics approximators.
• We pinpoint this challenge as the "revisitation" issue, where agents recurrently explore confined areas of the task space.
• To combat this, we propose a dynamic reward scaling approach.