United Kingdom

Active screening is a common approach in controlling the spread of recurring infectious diseases such as tuberculosis and influenza. In this approach, health workers periodically select a subset of population for screening. However, given the limited number of health workers, only a small subset of the population can be visited in any given time period. Given the recurrent nature of the disease and rapid spreading, the goal is to minimize the number of infections over a long time horizon. Active screening can be formalized as a sequential combinatorial optimization over the network of people and their connections. The main computational challenges in this formalization arise from i) the combinatorial nature of the problem, ii) the need of sequential planning and iii) the uncertainties in the infectiousness states of the population.    Previous works on active screening fail to scale to large time horizon while fully considering the future effect of current interventions. In this paper, we propose a novel reinforcement learning (RL) approach based on Deep Q-Networks (DQN), with several innovative adaptations that are designed to address the above challenges. First, we use graph convolutional networks (GCNs) to represent the Q-function that exploit the node correlations of the underlying contact network. Second, to avoid solving a combinatorial optimization problem in each time period, we decompose the node set selection as a sub-sequence of decisions, and further design a two-level RL framework that solves the problem in a hierarchical way. Finally, to speed-up the slow convergence of RL which arises from reward sparseness, we incorporate ideas from curriculum learning into our hierarchical RL approach. We evaluate our RL algorithm on several real-world networks. Results show that our RL algorithm can scale up to 10 times the problem size of state-of-the-art (the variant that considers the effect of future interventions but unscalable) in terms of planning time horizon. Meanwhile, it outperforms state-of-the-art (the variant that scales up but does not consider the effect of future interventions) by up to 33% in solution quality.

AAMAS 2021

Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

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AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

technical paper

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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We investigate a repeated two-player game setting where the column player is also a designer of the system, and has full control over payoff matrices. In addition, we assume that the row player uses a no-regret algorithm to efficiently learn how to adapt their strategy to the column player's behaviour over time. The goal of the column player is to guide her opponent into picking a mixed strategy which is preferred by the system designer. Therefore, she needs to: (i) design appropriate payoffs for both players; and (ii) strategically interact with the row player during a sequence of plays in order to guide her opponent to converge to the desired mixed strategy. To design appropriate payoffs, we propose a novel zero-sum game construction whose unique minimax solution contains the desired behaviour. We also propose another construction in which only the minimax strategy of the row player is unique. Finally, we propose a new game playing algorithm for the system designer and show that it can guide the row player to its minimax strategy, under the assumption that the row player adopts a stable  no-regret algorithm.

How to Guide a Non-Cooperative Learner to Cooperate: Exploiting No-Regret Algorithms in System Design

Artificial agents trained by deep reinforcement learning will likely encounter novel situations after deployment that were never seen during training. Our agent must be 
 to handle such situations well. However, if we cannot rely on the average training or validation reward as a metric, then how can we effectively evaluate robustness? We take inspiration from the practice of 
 in software engineering. Specifically, we suggest that when designing AI agents that collaborate with humans, designers should search for potential edge cases in 
 and 
 and write tests which check that the behavior of the agent in these edge cases is reasonable. We apply this methodology to build a suite of unit tests for the Overcooked-AI environment, and use this test suite to evaluate three proposals for improving robustness. We find that the test suite provides significant insight into the effects of these proposals that were generally not revealed by looking solely at the average validation reward. For our full paper, see https://arxiv.org/abs/2101.05507 arxiv.org/abs/2101.05507

Evaluating the Robustness of Collaborative Agents

We study the problem of zero-shot coordination (ZSC), where agents must independently produce strategies for a collaborative game that are compatible with novel partners not seen during training. In particular, our first contribution is to consider the need for diversity in generating such agents. Because self-play agents control their own trajectory distribution during training, their policy only performs well on this exact distribution. As a result, they achieve low scores in ZSC, since playing with another agent is likely to put them in situations they have not encountered during training. To address this issue, we train a common best response (BR) to a population of agents, which we regulate to be as diverse as possible. For that purpose, we introduce Trajectory Diversity (TrajeDi) - a differentiable objective for generating diverse reinforcement learning (RL) policies. We present TrajeDi as a generalization of the Jensen-Shannon divergence (JSD) between policies and motivate it experimentally in a simple matrix game, where it allows to find the unique ZSC-optimal solution.

Trajectory Diversity for Zero-Shot Coordination

In this work we present the Hanabi Open Agent Dataset (HOAD)— meant to address the current lack of Hanabi datasets, HOAD is an easily extensible, open-sourced, and comprehensive collection of existing Hanabi playing agents, all ported to the Hanabi Learning Environment (HLE). We give a description and analysis of each agent’s strategy, and we also show cross-play performance between all the agents, demonstrating both their high quality and diversity of strategy. These properties make HOAD especially well suited to studies involving meta-learning and transfer learning. Finally, we describe in detail an easy way to add new agents to HOAD regardless of the origin codebase of the agent and make our code and dataset publicly available at https://github.com/aronsar/hoad.

HOAD: The Hanabi Open Agent Dataset

The ability of agents to learn to communicate through interaction has been studied through emergent communication tasks. Previous works in this domain have studied the linguistic properties of the emergent languages like compositionality, generalization, and as well as the environmental pressures that shape them. However, most of these experiments require a considerable amount of shared training time between agents to communicate successfully. Our work highlights the problem of sample inefficiency of agents in population-based referential games and proposes an Action Advising framework to counter it.

Towards Sample Efficient Learners in Population based Referential Games through Action Advising

This paper is preoccupied with the following question: given a (possibly opaque) learning system, how can we understand whether its behaviour adheres to governance constraints? The answer can be quite simple: we just need to "ask" the system about it. We propose to construct an investigator agent to query a learning agent-- the suspect agent-- to investigate its adherence to a given ethical policy in the context of an information-seeking dialogue, modeled in formal argumentation settings. This formal dialogue framework is the main contribution of this paper. Through it, we break down compliance checking mechanisms into three modular components, each of which can be tailored to various needs in a vast amount of ways: an investigator agent, a suspect agent, and an acceptance protocol determining whether the responses of the suspect agent comply with the policy. This acceptance protocol presents a fundamentally different approach to aggregation: rather than using quantitative methods to deal with the non-determinism of a learning system, we leverage the use of argumentation semantics to investigate the notion of properties holding consistently. Overall, we argue that the introduced formal dialogue framework opens many avenues both in the area of compliance checking and in the analysis of properties of opaque systems.

Interrogating the Black Box: Transparency through Information-Seeking Dialogues

Consider a typical organization whose worker agents seek to collectively cooperate for its general betterment. However, each individual agent simultaneously seeks to act to secure a larger chunk than its co-workers of the annual increment in compensation, which usually comes from a fixed pot. As such, the agents in an organization must cooperate and compete. Another feature of many organizations is that a worker receives a bonus, which is often a fraction of previous year’s total profit. As such, the agent derives a reward that is also partly dependent on historical performance. How should the individual agent decide to act in this context? Few methods for the mixed cooperative-competitive setting have been presented in recent years, but these are challenged by problem domains whose reward functions additionally depend on historical information. Recent deep multi-agent reinforcement learning (MARL) methods using long short-term memory (LSTM) may be used, but these adopt a joint perspective to the interaction or require explicit exchange of information among the agents to promote cooperation, which may not be possible under competition. In this paper, we first show that the agent’s decision-making problem can be modeled as an interactive partially observable Markov decision process (I-POMDP) that captures the dynamic of a history-dependent reward. We present an interactive advantage actor-critic method (IA2C+), which combines the independent advantage actor-critic network with a belief filter that maintains a belief distribution over other agents’ models. Empirical results show that IA2C+ learns the optimal policy faster and more robustly than several baselines.

Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards

Multi-Agent Path Finding (MAPF) is an NP-hard problem that has important applications for distribution centers, traffic management and computer games, and it is still difficult for current solvers to solve large instances optimally. Bounded suboptimal solvers, such as Enhanced Conflict-Based Search (ECBS) and its variants, are more efficient than optimal solvers in finding a solution with suboptimality guarantees. ECBS is a tree search algorithm that expands the search tree by repeatedly selecting search tree nodes from a focal list. In this work, we propose to use machine learning (ML) to learn a node-selection strategy to speed up ECBS. In the first phase of our framework, we use imitation learning and curriculum learning to learn node-selection strategies iteratively for different numbers of agents from training instances. In the second phase, we deploy the learned models in ECBS and test their solving performance on unseen instances drawn from the same distribution as the training instances. We demonstrate that our solver, \ML, shows substantial improvement in terms of the success rates and runtimes over ECBS on five different types of grid maps from the MAPF benchmark.

Learning Node-Selection Strategies in Bounded-Suboptimal Conflict-Based Search for Multi-Agent Path Finding

We study several fairness notions in allocating indivisible chores (i.e., items with non-positive values): envy-freeness and its relaxations. For allocations under each fairness criterion, we establish their approximation guarantees for other fairness criteria. Under the setting of additive cost functions, our results show strong connections between these fairness criteria and, at the same time, reveal intrinsic differences between goods allocation and chores allocation. Furthermore, we investigate the efficiency loss under these fairness constraints and establish their prices of fairness.

Connections between Fairness Criteria and Efficiency for Allocating Indivisible Chores

In the facility location problem with continuous demands, where customers are continuously distributed on an area, a planner wants to locate the facilities and allocate the customers to their closest facilities under the proximity rule. In this work, we focus on the fairness and system efficiency from the facility's perspective. Each facility is assumed to have a preference (represented as valuation function) over the subsets of customers. For the fairness of facilities, we provide approximation guarantees for the proportionality and envy-freeness. For the efficiency, we study the utilitarian and egalitarian social welfare. In addition, we are interested in quantifying the possible trade-offs between meeting the fairness criteria and maximizing social welfare, measured by the price of fairness.

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How to Guide a Non-Cooperative Learner to Cooperate: Exploiting No-Regret Algorithms in System Design

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Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty

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Next from AAMAS 2021

How to Guide a Non-Cooperative Learner to Cooperate: Exploiting No-Regret Algorithms in System Design

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Who and When to Screen: Multi-Round Active Screening for Recurrent Infectious Diseases Under Uncertainty

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