Because reinforcement learning suffers from a lack of scalability, online
value (and Q-) function approximation has received increasing interest this
last decade. This contribution introduces a novel approximation scheme, namely
the Kalman Temporal Differences (KTD) framework, that exhibits the following
features: sample-efficiency, non-linear approximation, non-stationarity
handling and uncertainty management. A first KTD-based algorithm is provided
for deterministic Markov Decision Processes (MDP) which produces biased
estimates in the case of stochastic transitions. Than the eXtended KTD
framework (XKTD), solving stochastic MDP, is described. Convergence is analyzed
for special cases for both deterministic and stochastic transitions. Related
algorithms are experimented on classical benchmarks. They compare favorably to
the state of the art while exhibiting the announced features
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.
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