https://damien-ernst.be/info8003-1-optimal-decision-making-for-complex-problems/2026-03-31T12:14:08+00:00weekly0.6https://damien-ernst.be/teaching/genv0002-1-sustainable-energy/2026-02-18T10:49:51+00:00weekly0.6https://damien-ernst.be/damien-ernst/https://damien-ernst.be/wp-content/uploads/2024/03/9c8e0-a7968-drapeau-fr.jpgDrapeau FRhttps://damien-ernst.be/wp-content/uploads/2024/03/4b5f2-b016e-jlw-damien-ernst.jpgjlw-Damien Ernst2026-02-14T08:16:35+00:00weekly0.6https://damien-ernst.be/equipe/https://damien-ernst.be/wp-content/uploads/2024/03/10ed7-7f735-dsc4727-copy-1.jpgDSC4727 (zoom)https://damien-ernst.be/wp-content/uploads/2024/03/7e804-eec35-dsc4727-copy.jpgDSC4727https://damien-ernst.be/wp-content/uploads/2024/03/b0ee5-3860e-jlw-equipe-ernst-e1411049148754.jpgjlw-equipe ernst2026-02-02T21:10:59+00:00weekly0.6https://damien-ernst.be/2026/01/29/parallelisable-memory-recurrent-units/https://damien-ernst.be/wp-content/uploads/2026/01/main-1.pngmainhttps://damien-ernst.be/wp-content/uploads/2026/01/two_valleys.pngtwo_valleyshttps://damien-ernst.be/wp-content/uploads/2026/01/valley.pngvalleyhttps://damien-ernst.be/wp-content/uploads/2026/01/convergent.pngconvergenthttps://damien-ernst.be/wp-content/uploads/2026/01/main.pngSummary figureComparison between classical RNNs and 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neural networks, hidden states and beliefs in partially observable environments© Illustration by Philippe Lambrechts.https://damien-ernst.be/wp-content/uploads/2024/03/aa1b7-97406-bayes.pngBayes filterFigure 2: Bayes filter processing the history for computing the belief b and giving the optimal action a*. The Bayes filter updates the belief b based on the previous action a and the new observation o.https://damien-ernst.be/wp-content/uploads/2024/03/0fbae-83d36-rnn.pngRecurrent Neural NetworkFigure 1: RNN processing the history for computing the hidden state h and approximating the optimal action a. The RNN updates its hidden state h based on the previous action a and the new observation o.2024-02-07T15:36:10+00:00monthlyhttps://damien-ernst.be/2023/08/24/warming-up-recurrent-neural-networks-to-maximise-reachable-multistability-greatly-improves-learning/https://damien-ernst.be/wp-content/uploads/2024/03/11dfc-7c412-warmup.pngwarmupFigure 2. Left: Mean squared error obtained on the test set of a long-term information restitution dataset by different types of RNNs trained via supervised learning. Half of them has been pretrained with the warmup. Right: Mean number of episodes needed by different types of RNNs to reach the optimal policy on a long-term dependency RL environment. Half of them has been pretrained with the warmup.https://damien-ernst.be/wp-content/uploads/2024/03/31ec1-7a63b-vaa.pngvaaFigure 1. Evolution of the mean squared error (left) and the VAA (right) of different types of RNNs trained on a long-term information restitution 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