Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation

TL;DR

This paper introduces a novel multi-robot navigation method using centralized state-values in dueling networks within the CTDE framework, improving cooperation and performance in decentralized multi-robot reinforcement learning tasks.

Contribution

It proposes a new architecture that incorporates a centralized state-value network to enhance cooperation and learning efficiency in multi-robot mapless navigation.

Findings

Outperforms prior CTDE methods like VDN and QMIX.

Effective in scenarios with 2, 4, and 8 robots.

Improves sample efficiency and global state awareness.

Abstract

We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm. This problem is challenging when each robot considers its path without explicitly sharing observations with other robots and can lead to non-stationary issues in Deep Reinforcement Learning (DRL). The typical CTDE algorithm factorizes the joint action-value function into individual ones, to favor cooperation and achieve decentralized execution. Such factorization involves constraints (e.g., monotonicity) that limit the emergence of novel behaviors in an individual as each agent is trained starting from a joint action-value. In contrast, we propose a novel architecture for CTDE that uses a centralized state-value network to compute a joint state-value, which is used to inject global state information in the value-based updates of the agents. Consequently, each model computes its gradient update for the weights, considering the overall state of the environment. Our idea follows the insights of Dueling Networks as a separate estimation of the joint state-value has both the advantage of improving sample efficiency, while providing each robot information whether the global state is (or is not) valuable. Experiments in a robotic navigation task with 2 4, and 8 robots, confirm the superior performance of our approach over prior CTDE methods (e.g., VDN, QMIX).