Q-learning for history-based reinforcement learning
Daswani, Mayank; Sunehag, Peter; Hutter, Marcus
Description
We extend the Q-learning algorithm from the Markov Decision Process setting to problems where observations are non-Markov and do not reveal the full state of the world i.e. to POMDPs. We do this in a natural manner by adding l0 regularisation to the pathwise squared Q-learning objective function and then optimise this over both a choice of map from history to states and the resulting MDP parameters. The optimisation procedure involves a stochastic search over the map class nested with classical...[Show more]
Collections | ANU Research Publications |
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Date published: | 2013 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/65868 |
Source: | Journal of Machine Learning Research |
Access Rights: | Open Access |
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File | Description | Size | Format | Image |
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01_Daswani_Q-learning_for_history-based_2013.pdf | 299.15 kB | Adobe PDF |
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