Interpretability by Design for Efficient Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) aims at optimising several, often conflicting goals to improve the flexibility and reliability of RL in practical tasks. This is typically achieved by...
Lagged correlation-based deep learning for directional trend change prediction in financial time series
Trend change prediction in complex systems with a large number of noisy time series is a problem with many applications for real-world phenomena, with stock markets as a notoriously difficult to...
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator
We propose a new method for training an agent via an evolutionary strategy (ES), in which we iteratively improve a set of samples to imitate: Starting with a random set, in every iteration we...
The Voynich Manuscript is Written in Natural Language: The Pahlavi Hypothesis
The late medieval Voynich Manuscript (VM) has resisted decryption and was considered a meaningless hoax or an unsolvable cipher. Here, we provide evidence that the VM is written in natural...