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Title: Learning Expensive Coordination: An Event-Based Deep RL Approach

Paper content:
Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly.
However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league.
Therefore, the leader, i.e., the manager of the company or the league, needs to provide bonuses to followers for efficient coordination, which we call expensive coordination.
The main difficulties of expensive coordination are that
i) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses and
ii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.
In this work, we address this problem through an event-based deep RL approach.
Our main contributions are threefold.
(1) We model the leader's decision-making process as a semi-Markov Decision Process and propose a novel multi-agent event-based policy gradient to learn the leader's long-term policy.
(2) We exploit the leader-follower consistency scheme to design a follower-aware module and a follower-specific attention module to predict the followers' behaviors and make accurate response to their behaviors.
(3) We propose an action abstraction-based policy gradient algorithm to reduce the followers' decision space and thus accelerate the training process of followers.
Experiments in resource collections, navigation, and the predator-prey game reveal that our approach outperforms the state-of-the-art methods dramatically.
Deep Multi-Agent Reinforcement Learning (MARL) has been widely used in coordinating cooperative agents to jointly complete certain tasks where the agent is assumed to be selfless (fully cooperative), i.e., the agent is willing to sacrifice itself to maximize the team reward.
However, in many cases of the real world, the agents are self-interested, such as taxi drivers in a taxi company (fleets) and clubs in a league.
For instance, in the example of taxi fleets (Miao et al., 2016) , drivers may prefer to stay in the area with high customer demand to gain more reward.
It is unfair and not efficient to compel the taxi driver to selflessly contribute to the company, e.g., to stay in the low customer demand area.
Forcing the drivers to selflessly contribute may increase the income for the company in a short-term but it will finally causes the low efficient and unsustainable of that company in the long run because the unsatisfied drivers may be demotivated and even leave the company.
Another important example is that the government wants some companies to invest on the poverty area to achieve the fairness of the society, which may inevitably reduce the profits of companies.
Similar to previous example, the companies may leave when the government forces them to invest.
A better way to achieve coordination among followers and achieve the leader's goals is that the manager of the company or the government needs to provide bonuses to followers, like the taxi company pays extra bonuses for serving the customers in rural areas and the government provides subsidies for investing in the poverty areas, which we term as expensive coordination.
In this paper, we solve the large-scale sequential expensive coordination problem with a novel RL training scheme.
There are several lines of works related to the expensive coordination problem, including mechanism design (Nisan & Ronen, 2001 ) and the principal-agent model (Laffont & Martimort, 2009 ).
However, these works focus more on static decisions (each agent only makes a single decision).
To consider sequential decisions, the leader-follower MDP game (Sabbadin & Viet, 2013; 2016) and the RL-based mechanism design (Tang, 2017; Shen et al., 2017) are introduced but most of their works only focus on matrix games or small-scale Markov games, which cannot be applied to the case with the large-scale action or state space.
The most related work is M 3 RL (Shu & Tian, 2019) where the leader assigns goals and bonuses by using a simple attention mechanism (summing/averaging the features together) and mind (behaviors) tracking to predict the followers' behaviors and makes response to the followers' behaviors.
But they only consider the rule-based followers, i.e., followers with fixed preference, and ignore the followers' behaviors responding to the leader's policy, which significantly simplifies the problem and leads the unreasonability of the model.
In the expensive coordination problem, there are two critical issues which should be considered: 1) the leader's long-term decision process where the leader has to consider both the long-term effect of itself and long-term behaviors of the followers when determining his action to incentivise the coordination among followers, which is not considered in (Sabbadin & Viet, 2013; Mguni et al., 2019) ; and 2) the complex interactions between the leader and followers where the followers will adapt their policies to maximize their own utility given the leader's policy, which makes the training process unstable and hard, if not unable, to converge in large-scale environment, especially when the leader changes his actions frequently, which is ignored by (Tharakunnel & Bhattacharyya, 2007; Shu & Tian, 2019) .
In this work, we address these two issues in the expensive coordination problem through an abstraction-based deep RL approach.
Our main contributions are threefold.
(1) We model the leader's decision-making process as a semiMarkov Decision Process (semi-MDP) and propose a novel event-based policy gradient to learn the leader's policy considering the long-term effect (leader takes actions at important points rather than at each step to avoid myopic decisions.) (Section 4.1).
(2) A well-performing leader's policy is also highly dependent on how well the leader knows the followers.
To predict the followers' behaviors precisely, we show the leader-follower consistency scheme.
Based on the scheme, the follower-aware module, the follower-specific attention module, and the sequential decision module are proposed to capture these followers' behaviors and make accurate response to their behaviors (Section 4.2).
(3) To accelerate the training process, we propose an action abstraction-based policy gradient algorithm for the followers.
This approach is able to reduce followers' decision space and thus simplifies the interaction between the leader and followers as well as accelerates the training process of followers (Section 4.3).
Experiments in resource collections, navigation and predatorprey show that our method outperforms the state-of-the-art methods dramatically.
This paper proposes a novel RL training scheme for Stackelberg Markov Games with single leader and multiple self-interested followers, which considers the leader's long-term decision process and complicated interaction between followers with three contributions.
1) To consider the long-term effect of the leader's behavior, we develop an event-based policy gradient for the leader's policy.
2) To predict the followers' behaviors and make accurate response to their behaviors, we exploit the leader-follower consistency to design a novel follower-aware module and follower-specific attention mechanism.
3) We propose an action abstraction-based policy gradient algorithm to accelerate the training process of followers.
Experiments in resource collections, navigation, and predator-prey game reveal that our method outperforms the state-of-the-art methods dramatically.
We are willing to highlight that SMGs contribute to the RL (especially MARL) community with three key aspects: 1).
As we mentioned in the Introduction, most of the existing MARL methods assume that all the agents are willing to sacrifice themselves to maximize the total rewards, which is not true in many real-world non-cooperative scenarios.
On the contrary, our proposed method realistically assumes that agents are self-interested.
Thus, SMGs provide a new scheme focusing more on the self-interested agents.
We think this aspect is the most significant contribution to the RL community.
2).
The SMGs can be regarded as the multi-agent system with different roles (the leader and the followers) (Wilson et al., 2008) and our method provides a solution to that problem.
3).
Our methods also contribute to the hierarchical RL, i.e., it provides a non-cooperative training scheme between the high-level policy (the leaders) and the low-level policy (the followers), which plays an important role when the followers are self-interested.
Moreover, our EBPG also propose an novel policy gradient method for the temporal abstraction structure.
There are several directions we would like to investigate to further extend our SMG model:
i) we will consider multiple cooperative/competitive leaders and multiple self-interested followers, which is the case in the labor market,
ii) we will consider multi-level leaders, which is the case in the hierarchical organizations and companies and
iii) we will consider the adversarial attacks to our SMG model, which may induce extra cost to the leader for efficient coordination.
We believe that our work is a preliminary step towards a deeper understanding of the leader-follower scheme in both research and the application to society.

assistant: text: We propose an event-based policy gradient  to train the leader and an action abstraction policy gradient to train the followers in leader-follower Markov game.