A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games

This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear

Control of large distributed systems using games with pure strategy nash equilibria

Control mechanisms for optimisation in large distributed systems cannot be constructed based on traditional methods of control because they are typically characterised by distributed information and costly and/or noisy communication. Furthermore, noisy observations and dynamism are also inherent to these systems, so their control mechanisms need to be flexible, agile and robust in the face of these characteristics. In such settings, a good control mechanism should satisfy the following four design requirements: (i) it should produce high quality solutions, (ii) it should be robustness and flexibility in the face of additions, removals and failures of components, (iii) it should operate by making limited use of communication, and (iv) its operation should be computational feasible. Against this background, in order to satisfy these requirements, in this thesis we adopt a design approach based on dividing control over the system across a team of self–interested agents. Such multi–agent systems (MAS) are naturally distributed (matching the application domains in question), and by pursing their own private goals, the agents can collectively implement robust, flexible and scalable control mechanisms. In more detail, the design approach we adopt is (i) to use games with pure strategy Nash equilibria as a framework or template for constructing the agents’ utility functions, such that good solutions to the optimisation problem arise at the pure strategy Nash equilibria of the game, and (ii) to derive distributed techniques for solving the games for their Nash equilibria. The specific problems we tackle can be grouped into four main topics. First, we investigate a class of local algorithms for distributed constraint optimisation problems (DCOPs). We introduce a unifying analytical framework for studying such algorithms, and develop a parameterisation of the algorithm design space, which represents a mapping from the algorithms’ components to their performance according to each of our design requirements. Second, we develop a game–theoretic control mechanism for distributed dynamic task allocation and scheduling problems. The model in question is an expansion of DCOPs to encompass dynamic problems, and the control mechanism we derive builds on the insights from our first topic to address our four design requirements. Third, we elaborate a general class of problems including DCOPs with noisy rewards and state observations, which are realistic traits of great concern in real–world problems, and derive control mechanisms for these environments. These control mechanism allow the agents to either learn their reward functions or decide when to make observations of the world’s state and/or communicate their beliefs over the state of the world, in such a manner that they perform well according to our design requirements. Fourth, we derive an optimal algorithm for computing and optimising over pure strategy Nash equilibria in games with sparse interaction structure. By exploiting the structure present in many multi-agent interactions, this distributed algorithm can efficiently compute equilibria that optimise various criteria, thus reducing the computational burden on any one agent and operating using less communication than an equivalent centralised algorithms.For each of these topics, the control mechanisms that we derive are developed such that they perform well according to all four f our design requirements. In sum, by making the above contributions to these specific topics, we demonstrate that the general approach of using games with pure strategy Nash equilibria as a template for designing MAS produces good control mechanisms for large distributed systems

Solving Continuous Control via Q-learning

While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the associated high-dimensional action spaces. However, most actor-critic methods come at the cost of added complexity: heuristics for stabilisation, compute requirements and wider hyperparameter search spaces. We show that a simple modification of deep Q-learning largely alleviates these issues. By combining bang-bang action discretization with value decomposition, framing single-agent control as cooperative multi-agent reinforcement learning (MARL), this simple critic-only approach matches performance of state-of-the-art continuous actor-critic methods when learning from features or pixels. We extend classical bandit examples from cooperative MARL to provide intuition for how decoupled critics leverage state information to coordinate joint optimization, and demonstrate surprisingly strong performance across a variety of continuous control tasks

Editorial

calls & calendarEDITORIA

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference