Computational and Robotic Models of the Hierarchical Organization of Behavior 2013
DOI: 10.1007/978-3-642-39875-9_2
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Abstract: Behavioral modules are units of behavior providing reusable building blocks that can be composed sequentially and hierarchically to generate extensive ranges of behavior. Hierarchies of behavioral modules facilitate learning complex skills and planning at multiple levels of abstraction and enable agents to incrementally improve their competence for facing new challenges that arise over extended periods of time. This chapter focusses on two features of behavioral hierarchy that appear to be less well recognized… Show more

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Cited by 12 publications
(11 citation statements)
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“…sink and stove both off), or because they are truly irrelevant to subsequent activities. In saltatory MB-HRL, these points can allow the planning process to attend only to a small core set of environmental features, further lightening the computational load (see [ 11 , 22 ]). A special but important case of such abstraction involves continuous state spaces, where saltatory MB-HRL can permit discretization (see [ 23 ]).…”
Section: Model-based Hierarchical Reinforcement Learning: Computationmentioning
confidence: 99%
See 1 more Smart Citation
“…sink and stove both off), or because they are truly irrelevant to subsequent activities. In saltatory MB-HRL, these points can allow the planning process to attend only to a small core set of environmental features, further lightening the computational load (see [ 11 , 22 ]). A special but important case of such abstraction involves continuous state spaces, where saltatory MB-HRL can permit discretization (see [ 23 ]).…”
Section: Model-based Hierarchical Reinforcement Learning: Computationmentioning
confidence: 99%
“…In this article, we consider the relationship between model-based RL and another form of RL that has also recently become a topic of discussion in cognitive science and neuroscience, namely hierarchical reinforcement learning (HRL) [9][10][11]. The basic idea in HRL is to augment the set of actions available to the agent to include a set of temporally extended multi-action subroutines or skills.…”
Section: Introductionmentioning
confidence: 99%
“…As opposed to classical methods enabling robots to learn tool-use, as (Brown and Sammut, 2012) or (Schillaci et al, 2012), which consider tools as objects with affordances to learn using a symbolic representation, (Forestier and Oudeyer, 2016) does not necessitate this formalism and learns tool-use using simply parameterized skills, leveraging on a pre-defined task hierarchy. Barto et al (2013) showed that building complex actions made of lower-level actions according to the task hierarchy can bootstrap exploration by reaching interesting outcomes more rapidly. Temporal abstraction has also proven to enhance the learning efficiency of a deep reinforcement learner in Kulkarni et al (2016).…”
Section: Introductionmentioning
confidence: 99%
“…One area of future research is to augment no-cost rules with techniques used to increase the effectiveness of with-cost rules in very large state spaces. These techniques include the development of state abstractions and behavioral hierarchies (Sutton et al, 1999 ; Dietterich, 2000 ; Barto and Mahadevan, 2003 ; Ravindran and Barto, 2003 ; Mahadevan, 2010 ; Osentoski and Mahadevan, 2010 ; Barto et al, 2013a ) which should be applicable, in principle, to the no-cost rules we use here. We expect any limitations from scaling of our no-cost rules to be similar to those of with-cost RL rules.…”
Section: Discussionmentioning
confidence: 99%