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Title: Revisiting The Master-Slave Architecture In Multi-Agent Deep Reinforcement Learning

Paper content:
Many tasks in artificial intelligence require the collaboration of multiple agents.
We exam deep reinforcement learning for multi-agent domains.
Recent research efforts often take the form of two seemingly conflicting perspectives, the decentralized perspective, where each agent is supposed to have its own controller; and the centralized perspective, where one assumes there is a larger model controlling all agents.
In this regard, we revisit the idea of the master-slave architecture by incorporating both perspectives within one framework.
Such a hierarchical structure naturally leverages advantages from one another.
The idea of combining both perspective is intuitive and can be well motivated from many real world systems, however, out of a variety of possible realizations, we highlights three key ingredients, i.e. composed action representation, learnable communication and independent reasoning.
With network designs to facilitate these explicitly, our proposal consistently outperforms latest competing methods both in synthetics experiments and when applied to challenging StarCraft  micromanagement tasks.
Reinforcement learning (RL) provides a formal framework concerned with how an agent takes actions in one environment so as to maximize some notion of cumulative reward.
Recent years have witnessed successful application of RL technologies to many challenging problems, ranging from game playing [17; 21] to robotics BID8 and other important artificial intelligence (AI) related fields such as BID19 etc.
Most of these works have been studying the problem of a single agent.However, many important tasks require the collaboration of multiple agents, for example, the coordination of autonomous vehicles BID1 , multi-robot control BID12 , network packet delivery BID31 and multi-player games BID24 to name a few.
Although multi-agent reinforcement learning (MARL) methods have historically been applied in many settings [1; 31] , they were often restricted to simple environments and tabular methods.Motivated from the success of (single agent) deep RL, where value/policy approximators were implemented via deep neural networks, recent research efforts on MARL also embrace deep networks and target at more complicated environments and complex tasks, e.g. [23; 19; 4; 12] etc.
Regardless though, it remains an open challenge how deep RL can be effectively scaled to more agents in various situations.
Deep RL is notoriously difficult to train.
Moreover, the essential state-action space of multiple agents becomes geometrically large, which further exacerbates the difficulty of training for multi-agent deep reinforcement learning (deep MARL for short).From
the viewpoint of multi-agent systems, recent methods often take the form of one of two perspectives. That
is, the decentralized perspective where each agent has its own controller; and the centralized perspective where there exists a larger model controlling all agents. As a
consequence, learning can be challenging in the decentralized settings due to local viewpoints of agents, which perceive non-stationary environment due to concurrently exploring teammates. On the
other hand, under a centralized perspective, one needs to directly deal with parameter search within the geometrically large state-action space originated from the combination of multiple agents. BID0 StarCraft
and its expansion StarCraft: Brood War are trademarks of Blizzard Entertainment TM In this regard, we revisit the idea of master-slave architecture to combine both perspectives in a complementary manner. The master-slave
architecture is a canonical communication architecture which often effectively breaks down the original challenges of multiple agents. Such architectures
have been well explored in multi-agent tasks [18; 28; 15; 16] . Although our designs
vary from these works, we have inherited the spirit of leveraging agent hierarchy in a master-slave manner. That is, the master
agent tends to plan in a global manner without focusing on potentially distracting details from each slave agent and meanwhile the slave agents often locally optimize their actions with respect to both their local state and the guidance coming from the master agent. Such idea can be well
motivated from many real world systems. One can consider the
master agent as the central control of some organized traffic systems and the slave agents as each actual vehicles. Another instantiation
of this idea is to consider the coach and the players in a football/basketball team. However, although the
idea is clear and intuitive, we notice that our work is among the first to explicitly design master-slave architecture for deep MARL.Specifically, we instantiate our idea with policy-based RL methods and propose a multi-agent policy network constructed with the master-slave agent hierarchy. For both each slave agent
and the master agent, the policy approximators are realized using recurrent neural networks (RNN). At each time step, we can
view the hidden states/representations of the recurrent cells as the "thoughts" of the agents. Therefore each agent has
its own thinking/reasoning of the situation. While each slave agent takes
local states as its input, the master agent takes both the global states and the messages from all slave agents as its input. The final action output of each
slave agent is composed of contributions from both the corresponding slave agent and the master agent. This is implemented via a gated
composition module (GCM) to process and transform "thoughts" from both agents to the final action.We test our proposal (named MS-MARL) using both synthetic experiments and challenging StarCraft micromanagement tasks. Our method consistently outperforms
recent competing MARL methods by a clear margin. We also provide analysis to showcase
the effectiveness of the learned policies, many of which illustrate interesting phenomena related to our specific designs.In the rest of this paper, we first discuss some related works in Section 2. In Section 3, we introduce the detailed
proposals to realize our master-slave multi-agent RL solution. Next, we move on to demonstrate the effectiveness
of our proposal using challenging synthetic and real multi-agent tasks in Section 4. And finally Section 5 concludes this paper with discussions
on our findings. Before proceeding, we summarize our major contributions as
follows• We revisit the idea of master-slave architecture for deep MARL. The proposed instantiation effectively combines both the centralized
and decentralized perspectives of MARL.• Our observations highlight and verify that composable action representation
, independent master/slave reasoning and learnable communication in-between are key factors to be successful in MS-MARL.• Our proposal empirically outperforms recent state-of-the-art methods on both
synthetic experiments and challenging StarCraft micromanagement tasks, rendering it a novel competitive MARL solution in general.
As stated above, our MS-MARL proposal can leverage advantages from both the centralized perspective and the decentralized perspective.
Comparing with the latter, we would like to argue that, not only does our design facilitate regular communication channels between slave agents as in previous works, we also explicitly formulate an independent master agent reasoning based on all slave agents' messages and its own state.
Later we empirically verify that, even when the overall information revealed does not increase per se, an independent master agent tend to absorb the same information within a big picture and effectively helps to make decisions in a global manner.
Therefore compared with pure in-between-agent communications, MS-MARL is more efficient in reasoning and planning once trained.On the other hand, when compared with methods taking a regular centralized perspective, we realize that our master-slave architecture explicitly explores the large action space in a hierarchical way.
This is so in the sense that if the action space is very large, the master agent can potentially start searching at a coarse scale and leave the slave agents focus their efforts in a more fine-grained domain.
This not only makes training more efficient but also more stable in a similar spirit as the dueling Q-network design BID29 , where the master agent works as base estimation while leaving the slave agents focus on estimating the advantages.
And of course, in the perspective of applying hierarchy, we can extend master-slave to master-master-slave architectures etc.
In this paper, we revisit the master-slave architecture for deep MARL where we make an initial stab to explicitly combine a centralized master agent with distributed slave agents to leverage their individual contributions.
With the proposed designs, the master agent effectively learns to give high-level instructions while the local agents try to achieve fine-grained optimality.
We empirically demonstrate the superiority of our proposal against existing MARL methods in several challenging mutli-agent tasks.
Moreover, the idea of master-slave architecture should not be limited to any specific RL algorithms, although we instantiate this idea with a policy gradient method, more existing RL algorithms can also benefit from applying similar schemes.

assistant: text: We revisit the idea of the master-slave architecture in multi-agent deep reinforcement learning and outperforms state-of-the-arts.