A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health

Efforts to reduce maternal mortality rate, a key UN Sustainable Development target (SDG Target 3.1), rely largely on preventative care programs to spread critical health information to high-risk populations. These programs face two important challenges: efficiently allocating limited health resources to large beneficiary populations, and adapting to evolving policy priorities. While prior works in restless multi-armed bandit (RMAB) demonstrated success in public health allocation tasks, they lack flexibility to adapt to evolving policy priorities. Concurrently, Large Language Models (LLMs) have emerged as adept, automated planners in various domains, including robotic control and navigation. In this paper, we propose DLM: a Decision Language Model for RMABs. To enable dynamic fine-tuning of RMAB policies for challenging public health settings using human-language commands, we propose using LLMs as automated planners to (1) interpret human policy preference prompts, (2) propose code reward functions for a multi-agent RL environment for RMABs, and (3) iterate on the generated reward using feedback from RMAB simulations to effectively adapt policy outcomes. In collaboration with ARMMAN, an India-based public health organization promoting preventative care for pregnant mothers, we conduct a simulation study, showing DLM can dynamically shape policy outcomes using only human language commands as input.

Social Environment Design

Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making. This paper proposes a new research agenda towards this end by introducing Social Environment Design, a general framework for the use of AI for automated policy-making that connects with the Reinforcement Learning, EconCS, and Computational Social Choice communities. The framework seeks to capture general economic environments, includes voting on policy objectives, and gives a direction for the systematic analysis of government and economic policy through AI simulation. We highlight key open problems for future research in AI-based policy-making. By solving these challenges, we hope to achieve various social welfare objectives, thereby promoting more ethical and responsible decision making.

Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping

Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work motivates the use of potential-based reward shaping to reduce the computational burden of each RL sub-problem. This work serves as a proof-of-concept and we hope will inspire future developments towards computationally efficient IRL.

Towards Zero Shot Learning in Restless Multi-armed Bandits

Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently been studied from a multi-agent reinforcement learning perspective. Prior RMAB research suffers from several limitations, e.g., it fails to adequately address continuous states, and requires retraining from scratch when arms opt-in and opt-out over time, a common challenge in many real world applications. We address these limitations by developing a neural network-based pre-trained model (PreFeRMAB) that has general zero-shot ability on a wide range of previously unseen RMABs, and which can be fine-tuned on specific instances in a more sample-efficient way than retraining from scratch. Our model also accommodates general multi-action settings and discrete or continuous state spaces. To enable fast generalization, we learn a novel single policy network model that utilizes feature information and employs a training procedure in which arms opt-in and out over time. We derive a new update rule for a crucial $\lambda$-network with theoretical convergence guarantees and empirically demonstrate the advantages of our approach on several challenging, real-world inspired problems.

Offline Reinforcement Learning with Closed-Form Policy Improvement Operators

Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a policy is trained to maximize a learned value function while constrained by the behavior policy to avoid a significant distributional shift. In this paper, we propose our closed-form policy improvement operators. We make a novel observation that the behavior constraint naturally motivates the use of first-order Taylor approximation, leading to a linear approximation of the policy objective. Additionally, as practical datasets are usually collected by heterogeneous policies, we model the behavior policies as a Gaussian Mixture and overcome the induced optimization difficulties by leveraging the LogSumExp's lower bound and Jensen's Inequality, giving rise to a closed-form policy improvement operator. We instantiate offline RL algorithms with our novel policy improvement operators and empirically demonstrate their effectiveness over state-of-the-art algorithms on the standard D4RL benchmark.

LAD: Language Augmented Diffusion for Reinforcement Learning

Learning skills from language provides a powerful avenue for generalization in reinforcement learning, although it remains a challenging task as it requires agents to capture the complex interdependencies between language, actions, and states. In this paper, we propose leveraging Language Augmented Diffusion models as a planner conditioned on language (LAD). We demonstrate the comparable performance of LAD with the state-of-the-art on the CALVIN language robotics benchmark with a much simpler architecture that contains no inductive biases specialized to robotics, achieving an average success rate (SR) of 72% compared to the best performance of 76%. We also conduct an analysis on the properties of language conditioned diffusion in reinforcement learning.

Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset

We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the ongoing TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the highest-scoring runs in rounds 1, 2, and 3. In round 3, we report the highest-scoring run that takes advantage of previous training data and the second-highest fully automatic run.

Rapidly Bootstrapping a Question Answering Dataset for COVID-19

We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. To our knowledge, this is the first publicly available resource of its type, and intended as a stopgap measure for guiding research until more substantial evaluation resources become available. While this dataset, comprising 124 question-article pairs as of the present version 0.1 release, does not have sufficient examples for supervised machine learning, we believe that it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19. This paper describes our methodology for constructing the dataset and presents the effectiveness of a number of baselines, including term-based techniques and various transformer-based models. The dataset is available at http://covidqa.ai/

Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned

We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. This web application exists as part of a suite of tools that we have developed over the past few weeks to help domain experts tackle the ongoing global pandemic. We hope that improved information access capabilities to the scientific literature can inform evidence-based decision making and insight generation. This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.