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In safe Reinforcement Learning (RL), the agent attempts to find policies which maximize the expectation of accumulated rewards and guarantee its safety to remain above a given threshold. Hence, it is straightforward to formalize safe RL problems by both a reward function and a safety constraint. We define safety as the probability of survival in environments where taking risky actions could lead to early termination of the task. Although the optimization problem is already constrained by a safety threshold, reward signals related to unsafe terminal states influence the original maximization objective of the task. Selecting the appropriate value of these signals is often a time consuming and challenging reward engineering task, which requires expert knowledge of the domain.     This paper presents a safe RL algorithm, called Masked Constrained Policy Optimization (MCPO), in which the learning process is constrained by safety and excludes the risk reward signals. We develop MCPO as an extension of gradient-based policy search methods, in which the updates of the policy and the expected reward models are masked. Our method benefits from having a high probability of satisfying the given constraints for every policy in the learning process. We validate the proposed algorithm in two continuous tasks. Our findings prove the proposed algorithm is able to neglect risk reward signals, and thereby resolving the desired safety-performance trade-off without having the need for hand-tuning rewards.

AAMAS 2021

No More Hand-Tuning Rewards: Masked Constrained Policy Optimization for Safe Reinforcement Learning

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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technical paper

Agents can individually devise plans and coordinate to achieve common goals. Methods exist to factor planning problems into separate tasks and distribute the plan synthesis process, while reducing the overall planning complexity. Merging distributedly generated plans becomes computationally costly when task plans are tightly coupled, and conflicts arise due to dependencies between plan actions. New plan merging algorithms allow factoring and solving large problems with a growing number of agents and tasks, but are yet to be demonstrated in physical real-world systems.   This Demo presents an architecture that deploys plan merging algorithms in a physical multi-robot setting and emulates a First Response Domain.

Scalable Multiple Robot Task Planning with Plan Merging and Conflict Resolution

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 studies distributed Q-learning for Linear Quadratic Regulator (LQR) in a multi-agent network. The existing results often assume that agents can observe the global system state, which may be infeasible in large-scale systems due to privacy concerns or communication constraints. In this work, we consider a setting with unknown system models and no centralized coordinator. We devise a state tracking (ST) based Q-learning algorithm to design optimal controllers for agents. Specifically, we assume that agents maintain local estimates of the global state based on their local information and communications with neighbors. At each step, every agent updates its local global state estimation, based on which it solves an approximate Q-factor locally through policy iteration. Assuming a decaying injected excitation noise during the policy evaluation, we prove that the local estimation converges to the true global state, and establish the convergence of the proposed distributed ST-based Q-learning algorithm. The experimental studies corroborate our theoretical results by showing that our proposed method achieves comparable performance with the centralized case.

Distributed Q-Learning with State Tracking for Multi-agent Networked Control

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

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

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.

Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

This paper addresses an epidemiologic inference problem where, given realtime observation of test results, presence of symptoms, and physical contacts, the most likely infected individuals need to be inferred. The inference problem is modeled as a hidden Markov model where infection probabilities are updated at every time step and evolve between time steps. We suggest a unique inference approach that avoids storing the given observations explicitly. Theoretical justification for the proposed model is provided under specific simplifying assumptions. To complement these theoretical results, a comprehensive experimental study is performed using a custom-built agent-based simulator that models inter-agent contacts. The reported results show the effectiveness of the proposed inference model when considering more realistic scenarios -- where the simplifying assumptions do not hold. When pairing the proposed inference model with a simple testing and quarantine policy, promising trends are obtained where the epidemic progression is significantly slowed down while quarantining a bounded number of individuals.

Multiagent Epidemiologic Inference through Realtime Contact Tracing

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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Scalable Multiple Robot Task Planning with Plan Merging and Conflict Resolution

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Next from AAMAS 2021

Scalable Multiple Robot Task Planning with Plan Merging and Conflict Resolution

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Tunable Behaviours in Sequential Social Dilemmas using Multi-Objective Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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