By Christopher Gatti
This thesis takes an empirical method of realizing of the habit and interactions among the 2 major elements of reinforcement studying: the educational set of rules and the useful illustration of discovered wisdom. the writer techniques those entities utilizing layout of experiments now not normally hired to check computing device studying equipment. the implications 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 procedure could be utilized to tougher problems.
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Additional resources for Design of Experiments for Reinforcement Learning
Example text
1 Effectors of Reinforcement Learning Performance Despite the widely varying results of reinforcement learning in a variety of domains and circumstances, there is relatively little work explicitly investigating exactly what 40 2 Reinforcement Learning affects the performance of reinforcement learning with respect to domain characteristics, learning algorithms, and representations, as well as interactions between these components.
Recall that in reinforcement learning, the agent generally begins with no knowledge about the problem domain. In transfer and relational learning, knowledge that has been learned about one task is utilized to improve the learning process and efficiency in another, related task. A ‘related’ task can take different forms, some of which include sharing features (Konidaris et al. 2012), altering the allowable actions, altering the reward structure, or generalizing the applicability. This approach is also very similar to that of inductive learning from an artificial intelligence perspective (Michalski 1983), for which there has been some work that leverages this learning approach to develop agents that learn provably optimal solutions (Schmidhuber 2005, 2006).
Despite the success of this approach, the construction of the coding scheme requires a priori domain-specific knowledge and is not often straightforward (Ollington et al. 2009; Whiteson et al. 2011), and this approach becomes intractable with high-dimensional state spaces (Szepesvári 2010). A similar approach to tile coding is state aggregation in which similar states are grouped using a variety of methods, such as fuzzy clustering (Singh et al. 1995) or adaptive partitioning (Bonarini et al. 2007).