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ACAL
2015
Springer

Learning Options for an MDP from Demonstrations

4 years 11 months ago
Learning Options for an MDP from Demonstrations
Abstract. The options framework provides a foundation to use hierarchical actions in reinforcement learning. An agent using options, along with primitive actions, at any point in time can decide to perform a macro-action made out of many primitive actions rather than a primitive action. Such macro-actions can be hand-crafted or learned. There has been previous work on learning them by exploring the environment. Here we take a different perspective and present an approach to learn options from a set of experts demonstrations. Empirical results are also presented in a similar setting to the one used in other works in this area.
Marco Tamassia, Fabio Zambetta, William L. Raffe,
Added 27 Mar 2016
Updated 27 Mar 2016
Type Journal
Year 2015
Where ACAL
Authors Marco Tamassia, Fabio Zambetta, William L. Raffe, Xiaodong Li
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