Q-learning for history-based reinforcement learning
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|
|Source:||Journal of Machine Learning Research|
|01_Daswani_Q-learning_for_history-based_2013.pdf||299.15 kB||Adobe PDF|
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