United Kingdom

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.

AAMAS 2021

HOAD: The Hanabi Open Agent Dataset

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poster

In many community health settings, it is crucial to have a systematic monitoring and intervention process to ensure that the patients adhere to healthcare programs, such as periodic health checks or taking medications. When these interventions are expensive, they can be provided to only a fixed small fraction of the patients at any period of time. Hence, it is important to carefully choose the beneficiaries who should be provided with interventions and when. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention provided to them. In practice, the transition probabilities are unknown a priori, and hence, we propose a mechanism for the problem of balancing the explore-exploit trade-off. Empirically, we find that our proposed mechanism outperforms the baseline intervention scheme maternal healthcare dataset.

Learning Index Policies for Restless Bandits with Application to Maternal Healthcare

Learning in tightly coupled multiagent settings with sparse rewards is challenging because multiple agents must reach the goal state simultaneously for the team to receive a reward. This is even more challenging under temporal coupling constraints - where agents need to sequentially complete different components of a task in a particular order. Here, a single local reward is inadequate for learning an effective policy. We introduce MADyS, Multiagent Learning via Dynamic Skill Selection, a bi-level optimization framework that learns to dynamically switch between multiple local skills to optimize sparse team objectives. MADyS adopts fast policy gradients to learn local skills using local rewards and an evolutionary algorithm to optimize the sparse team objective by recruiting the most optimal skill at any given time. This eliminates the need to generate a single dense reward via reward shaping or other mixing functions. In environments with both spatial and temporal coupling requirements, we outperform prior methods and provides intuitive visualizations of its skill switching strategy.

Dynamic Skill Selection for Learning Joint Actions

Multi-agent reinforcement learning (RL) often struggles to ensure the safe behaviours of the learning agents, and therefore it is generally not adapted to safety-critical applications. To address this issue, we present a methodology that combines formal verification with (deep) RL algorithms to guarantee the satisfaction of formally-specified safety constraints both in training and testing. The approach we propose expresses the constraints to verify in \textit{Probabilistic Computation Tree Logic} (PCTL) and builds an abstract representation of the system to reduce the complexity of the verification step. This abstract model allows for model checking techniques to identify a set of abstract policies that meet the safety constraints expressed in PCTL. Then, the agents' behaviours are restricted according to these safe abstract policies.We provide formal guarantees that by using this method, the actions of the agents always meet the safety constraints, and provide a procedure to generate an abstract model automatically. We empirically evaluate and show the effectiveness of our method in a multi-agent environment.

An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors

Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents. Recent research has introduced successful methods to scale multi-agent reinforcement learning algorithms to many agent scenarios using mean field theory. Previous work in this field assumes that an agent has access to exact cumulative metrics regarding the mean field behaviour of the system, which it can then use to take its actions. In this paper, we relax this assumption and maintain a distribution to model the uncertainty regarding the mean field of the system. We consider two different settings for this problem. In the first setting, only agents in a fixed neighbourhood are visible, while in the second setting, the visibility of agents is determined at random based on distances. For each of these settings, we introduce a Q-learning based algorithm that can learn effectively. We prove that this Q-learning estimate stays very close to the Nash Q-value (under a common set of assumptions) for the first setting. We also empirically show our algorithms outperform multiple baselines in three different games in the MAgents framework, which supports large environments with many agents learning simultaneously to achieve possibly distinct goals.

Partially Observable Mean Field Reinforcement Learning

Recently, Jain et al. [IJCAI, 2019] studied the effect of project interactions in participatory budgeting (PB) by assuming an existing partition of the projects to interaction structures, namely a grouping of the projects into substitution and complementarity groups. Motivated by their study, here we take voter preferences to find such interaction structures. In our model, voters submit interaction structures, and the goal is to find an aggregated structure. Formally, given a set P of m projects, and n partitions of P, the task is to aggregate these n partitions into one aggregated partition.  We consider this partition aggregation task both for substitution structures and for complementarity structures, studying several aggregation methods for each, including utility-based methods and Condorcet-based methods; we evaluate these methods by analyzing their computational complexity and their behavior with respect to certain relevant axiomatic properties.

Partition Aggregation for Participatory Budgeting

We consider the problem of committee selection from a fixed set of candidates where each candidate has multiple quantifiable attributes. Instead of voting for a candidate, a voter is allowed to approve the preferred attributes of a given candidate. Though attribute-based preferences capture several important real-life scenarios, committee selection problem with attribute approval of voters has not properly formalized or studied. We present a detailed study of axioms, rules, algorithms, and computational complexity for the setting. Apart from extending previous axioms and rules, we design two new algorithms that are especially appropriate for our model.

Committee Selection using Attribute Approvals

Shortlisting is the task of reducing a long list of alternatives to a (smaller) set of best or most suitable alternatives from which a final winner will be chosen. Shortlisting is often used in the nomination process of awards or in recommender systems to display featured objects. In this paper, we analyze shortlisting methods that are based on approval data, a common type of preferences. Furthermore, we assume that the size of the shortlist, i.e., the number of best or most suitable alternatives, is not fixed but determined by the shortlisting method. We axiomatically analyze established and new shortlisting methods and complement this analysis with an experimental evaluation based on imperfect quality estimates. Our results lead to recommendations which shortlisting methods to use, depending on the desired properties.

Approval-Based Shortlisting

We model interpretive blindness, a type of epistemic bias that poses a problem for learning from testimony, in which one acquires information from text or conversation but lacks direct access to ground truth. Interpretive blindness arises when a co-dependence between background beliefs and interpretation leads to a dynamic process of bias hardening that impedes or precludes learning. We argue that when bodies of data are argumentatively complete, even constraints from hierarchical Bayesian learning designed to promote good epistemic practices will fail to stop interpretive blindness.

Interpretive Blindness and the Impossibility of Learning from Testimony

We present the first explicit-state method for analysing and ensuring the safety of DRL agents for Atari games. Our method only requires access to the emulator. We give a suite of 42 properties that characterise "safe behaviour" for 31 games. We evaluate the safety of the best available DRL agents which, as our experiments show, violate most of our properties. We propose a countermeasure that implements shielding using bounded explicit-state exploration. Our method improved their overall safety, producing the safest DRL agents for Atari games currently available.

Shielding Atari Games with Bounded Prescience

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.

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Learning Index Policies for Restless Bandits with Application to Maternal Healthcare

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