Results 1 
2 of
2
Learning to Act Stochastically
"... This thesis examines reinforcement learning for stochastic control processes with single and multiple agents, where either the learning outcomes are stochastic policies or learning is perpetual and within the domain of stochastic policies. In this context, a policy is a strategy for processing envir ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
This thesis examines reinforcement learning for stochastic control processes with single and multiple agents, where either the learning outcomes are stochastic policies or learning is perpetual and within the domain of stochastic policies. In this context, a policy is a strategy for processing environmental outputs (called observations) and subsequently generating a response or inputsignal to the environment (called actions). A stochastic policy gives a probability distribution over actions for each observed situation, and the thesis concentrates on finite sets of observations and actions. There is an exclusive focus on stochastic policies for two principle reasons: such policies have been relatively neglected in the existing literature, and they have been recognised to be especially important in the field of multiagent reinforcement learning. For the latter reason, the thesis concerns itself primarily with solutions best suited to multiagent domains. This restriction proves essential, since the topic is otherwise too broad to be covered in depth without losing some clarity and focus. The thesis is partitioned into 3 parts, with chapter of contextual information preceding the first part. Part 1, focuses on analytic and formal mathematical approaches
Modelling MAS with Finite Analytic Stochastic Processes
"... Abstract. The MultiAgent paradigm is becoming increasingly popular as a way of capturing complex control processes with stochastic properties. Many existing modelling tools are not flexible enough for these purposes, possibly because many of the modelling frameworks available inherit their structur ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract. The MultiAgent paradigm is becoming increasingly popular as a way of capturing complex control processes with stochastic properties. Many existing modelling tools are not flexible enough for these purposes, possibly because many of the modelling frameworks available inherit their structure from single agent frameworks. This paper proposes a new family of modelling frameworks called FASP, which is based on state encapsulation and powerful enough to capture multiagent domains. It identifies how the FASP is more flexible, and describes systems more naturally than other approaches, demonstrating this with a number of robot football (soccer) formulations. This is important because more natural descriptions give more control when designing the tasks, against which a group of agents’ collective behaviour is evaluated and regulated. 1