This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variables. This lemma provides an alternative tool to Hoeffding-Azuma inequality to bound concentration of martingale values. Our second approach is based on integration of Hoeffding-Azuma inequality with PAC-Bayesian analysis. We also introduce a way to apply PAC-Bayesian analysis in situation of limited feedback. We combine the new tools to derive PAC-Bayesian generalization and regret bounds for the multiarmed bandit problem. Although our regret bound is not yet as tight as state-of-the-art regret bounds based on other well-established techniques, our results significantly expand the range of potential applications of PAC-Bayesian analysis and introduce a new analysis tool to reinforcement learning and many other fields, where martingales and limited feedback are encountered.

The Cultural Geography (CG) model is a governmentowned, open-source agent-based model designed to Balancing the ratio of exploration and exploitation is an address the behavioral response of civilian populations in important problem in reinforcement learning [1]. If you conflict environments [3]. Agents within the CG Model examine the relationship between agent and the select their action according to a constant temperature environment in reinforcement learning, agent has two setting over the course of a model run. To enhance the action selections in its environment: exploration and functionality of agents in selecting their actions and to get exploitation. The agent can choose to explore its more realistic results with better utilities we changed this environment and try new actions in search for better ones constant to a dynamic parameter which depends on time in to be adopted in the future, or exploit already tested actions Time Based Selection and on utility in Aggregate Utility and adopt them.

In this paper, we present a Personalized Intelligent Tutoring System that uses Reinforcement Learning techniques to implicitly learn teaching rules and provide instructions to students based on their needs. The system works on coarsely labeled data with minimum expert knowledge to ease extension to newer domains.

Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.

A principal one among them is the existence of multiple domains that share the same underlying causal structure for actions. We describe an approach that exploits this shared causal structure to discover a hierarchical task structure in a source domain, which in turn speeds up learning of task execution knowledge in a new target domain. Our approach is theoretically justified and compares favorably to manually designed task hierarchies in learning efficiency in the target domain. We demonstrate that causally motivated task hierarchies transfer more robustly than other kinds of detailed knowledge that depend on the idiosyncrasies of the source domain and are hence less transferable.

Sequential decision tasks present many opportunities for the study of transfer learning. A principal one among them is the existence of multiple domains that share the same underlying causal structure for actions. We describe an approach that exploits this shared causal structure to discover a hierarchical task structure in a source domain, which in turn speeds up learning of task execution knowledge in a new target domain. Our approach is theoretically justified and compares favorably to manually designed task hierarchies in learning efficiency in the target domain. We demonstrate that causally motivated task hierarchies transfer more robustly than other kinds of detailed knowledge that depend on the idiosyncrasies of the source domain and are hence less transferable.

Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area.

This work introduces Human-Agent Transfer (HAT), an algorithm that combines transfer learning, learning from demonstration and reinforcement learning to achieve rapid learning and high performance in complex domains. Using experiments in a simulated robot soccer domain, we show that human demonstrations transferred into a baseline policy for an agent and refined using reinforcement learning significantly improve both learning time and policy performance. Our evaluation compares three algorithmic approaches to incorporating demonstration rule summaries into transfer learning, and studies the impact of demonstration quality and quantity. Our results show that all three transfer methods lead to statistically significant improvement in performance over learning without demonstration.

This paper describes a framework for an agent to learn verb-phrase meanings from human teachers and combine these models with environmental dynamics so the agent can enact verb commands from the human teacher. This style of human/agent interaction allows the human teacher to issue natural-language commands and demonstrate ground actions, thereby alleviating the need for advanced teaching interfaces or difficult goal encodings. The framework extends prior work in apprenticeship learning and builds off of recent advancements in learning to recognize activities and modeling domains with multiple objects. In our studies, we show how to both learn a verb model and turn it into reward and heuristic functions that can then be composed with a dynamics model. The resulting "combined model" can then be efficiently searched by a sample-based planner which determines a policy for enacting a verb command in a given environment. Our experiments with a simulated robot domain show this framework can be used to quickly teach verb commands that the agent can then enact in new environments.