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A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks (2007.04979v1)

Published 9 Jul 2020 in cs.CV, cs.AI, cs.LG, and cs.MA

Abstract: Autonomous agents must learn to collaborate. It is not scalable to develop a new centralized agent every time a task's difficulty outpaces a single agent's abilities. While multi-agent collaboration research has flourished in gridworld-like environments, relatively little work has considered visually rich domains. Addressing this, we introduce the novel task FurnMove in which agents work together to move a piece of furniture through a living room to a goal. Unlike existing tasks, FurnMove requires agents to coordinate at every timestep. We identify two challenges when training agents to complete FurnMove: existing decentralized action sampling procedures do not permit expressive joint action policies and, in tasks requiring close coordination, the number of failed actions dominates successful actions. To confront these challenges we introduce SYNC-policies (synchronize your actions coherently) and CORDIAL (coordination loss). Using SYNC-policies and CORDIAL, our agents achieve a 58% completion rate on FurnMove, an impressive absolute gain of 25 percentage points over competitive decentralized baselines. Our dataset, code, and pretrained models are available at https://unnat.github.io/cordial-sync .

Citations (51)

Summary

  • The paper introduces SYNC policies and a novel Coordination Loss to overcome limitations of marginal policies in complex multi-agent embodied tasks.
  • It proposes a challenging 'Furniture Moving' task within a photorealistic environment, requiring continuous, tightly-coupled coordination between agents.
  • The proposed methods achieve a 58% task completion rate, representing a 25 percentage point improvement over decentralized baselines in the new benchmark.

Overview of "A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks"

The paper "A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks" explores and addresses the challenges of enabling autonomous agents to collaborate in visually rich environments. Unlike grid-world scenarios where multi-agent systems have been extensively studied, this work focuses on embodied tasks requiring agents to coordinate their actions continuously and precisely. The authors present a new task in which agents must cooperate to move a piece of furniture through a living room, emphasizing the need for coordination at every timestep due to constraints like physical space and agent embodiment.

Key Contributions:

  1. Introduction of a Challenging Task: The paper introduces a novel multi-agent task, titled 'Furniture Moving,' set in the photo-realistic AI2-THOR environment. Agents receive only their egocentric visual views to navigate, hold, and move a piece of furniture to a goal. This task differentiates itself by necessitating ongoing coordination among agents, given constraints such as potential collisions, mutual occlusion, and limited communication bandwidth.
  2. Addressing Policy Constraints: The authors identify that existing approaches relying on decentralized action sampling often limit the representation of joint policies to rank-one tensors. This constraint significantly reduces the expressiveness and efficacy of multi-agent systems in tightly coupled tasks. To address this, the authors propose SYNC policies (Synchronize Your actioNs Coherently), which enable agents to approximate higher-rank joint policies through synchronized sampling using shared strategies.
  3. Coordination Loss (CL): To overcome the challenge where the number of failed actions outpaces successful ones, particularly as the number of agents increases, the paper introduces the Coordination Loss. This loss, integrated into policy gradient methods, aims to guide agents towards coordinated actions, effectively replacing the traditional entropy regularization with an emphasis on synchronized policies.
  4. Performance Improvements: The proposed SYNC policies and Coordination Loss achieve a 58% completion rate in the introduced task, an absolute gain of 25 percentage points compared to competitive decentralized baselines. This underscores the potential to significantly enhance multi-agent collaboration through advanced policy structures and loss functions.
  5. Open Source Resources: The paper enriches the community with an enhanced AI2-THOR environment, dataset, code, and pretrained models to facilitate further exploration and advancements in multi-agent embodied AI.

Implications and Future Directions:

The advancements presented in this paper have substantial implications for the practical deployment of collaborative embodied agents in complex environments. The ability to capture expressive joint policies through SYNC and coordinate effectively can extend to numerous real-world applications, from assistive robotics in household settings to cooperative AI in industrial tasks. Future developments could include scaling the proposed methods to accommodate larger teams of agents, refining communication protocols, and extending investigations to more diverse tasks.

Additionally, the move towards exploring and addressing the representational limitations of joint policies in multi-agent systems opens avenues for richer, more adaptable AI behaviors. The intersection of communication, policy learning, and embodied constraints will likely fuel ongoing research, with particular interest in reducing dependency on centralized control and enhancing decentralized agent autonomy.

In summary, "A Cordial Sync" proposes significant methodological enhancements, underpinned by compelling empirical results, demonstrating the feasibility of superior agent coordination in challenging real-world inspired tasks. This work contributes critically to the field of multi-agent systems, highlighting the importance of policy expressiveness and synchronization as key levers for effective collaboration.

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