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Title: M^3RL: Mind-aware Multi-agent Management Reinforcement Learning

Paper content:
Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly learning a policy for each agent to maximize a common reward.
In this paper, we aim to address this from a different angle.
In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not want to do.
For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together.
The objective of the manager is to maximize the overall productivity as well as minimize payments made to the workers for ad-hoc worker teaming.
To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL), which consists of agent modeling and policy learning.
We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents.
The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation.
As the main assumption and building block in economics, self-interested agents play a central roles in our daily life.
Selfish agents, with their private beliefs, preferences, intentions, and skills, could collaborate (ad-hoc teaming) effectively to make great achievement with proper incentives and contracts, an amazing phenomenon that happens every day in every corner of the world.However, most existing multi-agent reinforcement learning (MARL) methods focus on collaboration when agents selflessly share a common goal, expose its complete states and are willing to be trained towards the goal.
While this is plausible in certain games, few papers address the more practical situations, in which agents are self-interested and inclined to show off, and only get motivated to work with proper incentives.In this paper, we try to model such behaviors.
We have multiple workers and a manager, together to work on a set of tasks.
The manager gets an external reward upon the completion of some tasks, or one specific task.
Each worker has a skill set and preference over the tasks.
Note that their skills and preferences may not align with each other ( Fig. 1(a
) ), and are not known to the manager ( Fig. 1(b
) ). Furthermore
, manager may not get any external reward until a specific task is complete, which depends on other tasks.By default, the self-interested workers simply choose the most preferred tasks, which is often unproductive from the perspective of the entire project. Therefore,
the manager gives additional incentives in the form of contracts. Each contract
assigns a goal and a bonus for achieving the goal to a worker. Figure 1: Illustration
of our problem setup. Workers have different
skills (abilities for completing tasks) and preferences (which tasks they like) indicated by the bar charts. They are self-interested
and perform the tasks they prefer the most. To achieve optimal collaboration
, a manager has to first infer workers' minds, and assigns right bonuses to workers for finishing specified tasks in the form of contracts. Consequently, workers will adjust
their intentions and work together accordingly. E.g., workers in the figure initially
all want to do task B. To finish all tasks, the manager has to pay more bonus to worker 1 and 2 so that they will perform A and C respectively.With the external incentives, workers may choose different goals than their preferences. Upon completion of assigned goals, the
manager receives the rewards associated with those goals and makes the promised payments to the workers. To generate optimal contracts, the manager
must infer the workers' minds and learn a good policy of goal and reward assignment.Conventional approaches of mechanism design tackle similar problems by imposing strong assumptions (e.g., skill/preference distributions, task dependencies, etc) to find an analytic solution. In contrast, we aim to train a manager using
reinforcement learning to i) assess minds of workers (skills, preferences
, intentions, etc.) on the fly, ii) to optimally assign contracts to maximize
a collaborative reward, and iii) is adapted to diverse and even evolving
workers and environments.For this, we propose a novel framework -Mind-aware Multi-agent Management Reinforcement Learning (M 3 RL), which entails both agent modeling for estimating workers' minds and policy learning for contract generation. For agent modeling, we infer workers' identities
by their performance history, and track their internal states with a mind tracker trained by imitation learning (IL). For contract generation, we apply deep reinforcement
learning (RL) to learn goal and bonus assignment policies. To improve the learning efficiency and adaptation, we
also propose high-level successor representation (SR) learning BID17 and agent-wise -greedy exploration.As a proof of concept, we evaluate our approach in two environments: Resource Collection and Crafting in 2D Minecraft, to simulate multi-agent management problems. The setup and underlying assumptions are designed to
mimic real world problems, where workers are not compelled to reveal their true preferences and skills, and there may be dependency between tasks resulting in delayed and sparse reward signals. Workers may also be deceitful (e.g., accepting a contract
even when the assigned goal is unreachable). Our experiments demonstrate that the manager trained by our
approach can i) estimate the mind of each worker from the recent behaviors
, ii) motivate the workers to finish less preferable or intermediate
tasks by assigning the right bonuses, iii) is adaptive to changing teams, e.g., change of members and/or
change of workers' skills and preferences, iv) and has good generalization in different team sizes and novel
environments.We have conducted substantial ablation studies by removing the key components, including IL, SR, agent-wise -greedy exploration, and performance history. Our approach shows a consistent performance in standard settings
as well as in more challenging ones where workers' policies are stochastic and sub-optimal, or there are multiple levels of bonuses required to motivate workers.
In this paper, we propose Mind-aware Multi-agent Management Reinforcement Learning (M 3 RL) for solving the collaboration problems among self-interested workers with different skills and preferences.
We train a manager to simultaneously infer workers' minds and optimally assign contracts to workers for maximizing the overall productivity, for which we combine imitation learning and reinforcement learning for a joint training of agent modeling and management policy optimization.
We also improve the model performance by a few techniques including learning high-level successor representation, agent-wise -greedy exploration, and agent identification based on performance history.
Results from extensive experiments demonstrate that our approach learns effectively, generalizes well, and has a fast and continuous adaptation.

assistant: text: We propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL) for training a manager to motivate self-interested workers to achieve optimal collaboration by assigning suitable contracts to them.