Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem (2411.12246v1)

Abstract: Self-organizing systems consist of autonomous agents that can perform complex tasks and adapt to dynamic environments without a central controller. Prior research often relies on reinforcement learning to enable agents to gain the skills needed for task completion, such as in the box-pushing environment. However, when agents push from opposing directions during exploration, they tend to exert equal and opposite forces on the box, resulting in minimal displacement and inefficient training. This paper proposes a model called Shared Pool of Information (SPI), which enables information to be accessible to all agents and facilitates coordination, reducing force conflicts among agents and enhancing exploration efficiency. Through computer simulations, we demonstrate that SPI not only expedites the training process but also requires fewer steps per episode, significantly improving the agents' collaborative effectiveness.

• The paper presents the Shared Pool of Information (SPI) model that enables agents to coordinate implicitly in box-pushing tasks without direct communication.
• SPI enhances training efficiency by reducing counterproductive force conflicts and minimizing the steps required to reach goals.
• Experimental results demonstrate that SPI consistently outperforms random exploration, achieving higher success rates and rewards under varied speed factors.

Efficient Training in Multi-Agent Reinforcement Learning: A Communication-Free Framework for the Box-Pushing Problem

This paper investigates the problem of coordination in multi-agent reinforcement learning (MARL) within the context of the box-pushing problem. Traditional MARL approaches often face inefficiencies when agents exert equal and opposite forces, resulting in minimal movement and consequently prolonging the training process. The authors propose an innovative Shared Pool of Information (SPI) model, which facilitates implicit coordination without necessitating direct inter-agent communication.

Overview of Methodology

In the box-pushing task, agents collaborate to push a box toward a goal while avoiding obstacles. The agents, which cannot sense one another, act solely based on limited environmental observations and learned experiences. The SPI model provides a novel indirect coordination mechanism by offering all agents access to a common base of information at initialization. This shared information serves as a foundation upon which agents build their exploration strategies, akin to the concept of common knowledge.

The SPI model comprises two primary components: a map and a key. The map offers a distribution of potential actions derived from simulations assuming centralized control, while the key aids in the pseudo-random selection of these actions, enabling decentralized execution. To ensure effective tool cooperation, SPI undergoes rigorous fitness testing, consisting of origin avoidance and angular spread uniformity assessments, which optimize the coordinate framework by reducing counterproductive force conflicts.

Experimental Results

The feasibility and efficacy of SPI are validated through rigorous simulations using varying speed factors, $\frac{1}{2}$ and $\frac{1}{3}$, for agent force exertion. Under these experimental conditions, SPI demonstrates an improvement over the traditional random exploration methods in several metrics:

1. Success Rate: The SPI framework achieved higher success rates than random exploration, particularly pronounced at the $\frac{1}{3}$ speed factor.
2. Efficiency: Notably, SPI consistently required fewer steps to reach the goal, indicating greater training efficiency. The success steps metric highlighted the ability of SPI agents to discover and adopt more efficient paths.
3. Reward: SPI agents consistently garnered higher rewards, further indicating superior performance over random exploration, especially at reduced speed factors.

Implications and Future Directions

The implications of using SPI in MARL systems are notable. It underscores the potential for efficient, scalable agent training in scenarios where inter-agent communication is impractical due to bandwidth constraints or other limitations. This methodology is particularly applicable in fields like swarm robotics, where rapid learning and coordination are crucial.

Moving forward, future research could delve into adapting SPI for environments that penalize collaboration or require minimal joint effort. Exploring broader action and state spaces might also yield insights, especially interesting when considering more heterogeneous and dynamic environments. These adaptations could further bolster SPI's applicability and efficacy across diverse MARL challenges.

Conclusion

Overall, this research presents a compelling case for the integration of a communication-free, shared informational framework in MARL settings. By leveraging SPI, agents can quickly learn and execute collaborative strategies in complex environments, showcasing the immense potential of decentralized and implicitly coordinated multi-agent systems.