Design of Experiments for Reinforcement Learning by Christopher Gatti

By Christopher Gatti

This thesis takes an empirical method of knowing of the habit and interactions among the 2 major parts of reinforcement studying: the training set of rules and the sensible illustration of realized wisdom. the writer ways those entities utilizing layout of experiments no longer regularly hired to check computing device studying equipment. the consequences defined during this paintings offer perception as to what allows and what has an impact on profitable reinforcement studying implementations in order that this studying technique will be utilized to more difficult problems.

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Finally, the use of neural networks for reinforcement learning in the real-world (including real-world conceptual problems) includes applications such as control problems (Werbos 1989; Mitchell and Thrun 1992; Yamada 2011), stock price prediction (Lee 2001) and trading (Gorse 2011), product delivery and distribution (Proper and Tadepalli 2006), resource allocation (Tesauro et al. 2007), jobshop scheduling (Gabel and Riedmiller 2007), and technical process control (Hafner and Riedmiller 2011). 3 Learning Algorithms The goal of learning algorithms in reinforcement learning is essentially to allow the agent to learn the dynamics of the environment so that an optimal set of actions may be selected to achieve a goal or obtain the greatest total reward.

The simplest linear approximators are based on hand-crafted features of the state space, and this can be successful in very simple problems. One of the prominent linear methods used in reinforcement learning is tile coding, which consists of multiple (and possibly irregular) tilings that overlap and cover the entire state space (Albus 1975; Barto 1990; Sutton and Barto 1998). This method can be thought of as a discretized radial basis function (RBF) network, and it therefore has the ability to generalize to small and localized regions of the state space (Skelly 2004).

The first purpose is to learn about the dynamics of the domain and how the selection of actions relate to feedback delivered by the domain. In other 30 2 Reinforcement Learning words, the purpose of this task is to learn an input-output mapping between the state space X, the action set A, and the feedback from the domain R. The same representation can be used for the second purpose, which is to evaluate the possible actions and select a single action to pursue. The learning algorithm we focus on uses a single entity to perform both of these functions, although algorithms may use distinct components for each of these tasks.

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