8,193 research outputs found
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms â a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms
Rational bidding using reinforcement learning: an application in automated resource allocation
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms â one centralized and one decentralized
IMAP: Intrinsically Motivated Adversarial Policy
Reinforcement learning agents are susceptible to evasion attacks during
deployment. In single-agent environments, these attacks can occur through
imperceptible perturbations injected into the inputs of the victim policy
network. In multi-agent environments, an attacker can manipulate an adversarial
opponent to influence the victim policy's observations indirectly. While
adversarial policies offer a promising technique to craft such attacks, current
methods are either sample-inefficient due to poor exploration strategies or
require extra surrogate model training under the black-box assumption. To
address these challenges, in this paper, we propose Intrinsically Motivated
Adversarial Policy (IMAP) for efficient black-box adversarial policy learning
in both single- and multi-agent environments. We formulate four types of
adversarial intrinsic regularizers -- maximizing the adversarial state
coverage, policy coverage, risk, or divergence -- to discover potential
vulnerabilities of the victim policy in a principled way. We also present a
novel Bias-Reduction (BR) method to boost IMAP further. Our experiments
validate the effectiveness of the four types of adversarial intrinsic
regularizers and BR in enhancing black-box adversarial policy learning across a
variety of environments. Our IMAP successfully evades two types of defense
methods, adversarial training and robust regularizer, decreasing the
performance of the state-of-the-art robust WocaR-PPO agents by 34%-54% across
four single-agent tasks. IMAP also achieves a state-of-the-art attacking
success rate of 83.91% in the multi-agent game YouShallNotPass
Agent-based simulation of electricity markets: a literature review
Liberalisation, climate policy and promotion of renewable energy are challenges to players of the electricity sector in many countries. Policy makers have to consider issues like market power, bounded rationality of players and the appearance of fluctuating energy sources in order to provide adequate legislation. Furthermore the interactions between markets and environmental policy instruments become an issue of increasing importance. A promising approach for the scientific analysis of these developments is the field of agent-based simulation. The goal of this article is to provide an overview of the current work applying this methodology to the analysis of electricity markets. --
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