Now showing 1 - 3 of 3
  • Publication
    Leveraging Domain Knowledge for Reinforcement Learning using MMC Architectures
    Despite the success of reinforcement learning methods in various simulated robotic applications, end-to-end training suffers from extensive training times due to high sample complexity and does not scale well to realistic systems. In this work, we speed up reinforcement learning by incorporating domain knowledge into policy learning. We revisit an architecture based on the mean of multiple computations (MMC) principle known from computational biology and adapt it to solve a reacher task. We approximate the policy using a simple MMC network, experimentally compare this idea to end-to-end deep learning architectures, and show that our approach reduces the number of interactions required to approximate a suitable policy by a factor of ten.
  • Publication
    Policy learning using SPSA
    We analyze the use of simultaneous perturbation stochastic approximation (SPSA), a stochastic optimization technique, for solving reinforcement learning problems. In particular, we consider settings of partial observability and leverage the short-term memory capabilities of echo state networks (ESNs) to learn parameterized control policies. Using SPSA, we propose three different variants to adapt the weight matrices of an ESN to the task at hand. Experimental results on classic control problems with both discrete and continuous action spaces reveal that ESNs trained using SPSA approaches outperform conventional ESNs trained using temporal difference and policy gradient methods.
  • Publication
    Using echo state networks for cryptography
    Echo state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate sequences of data. We make use of this property to realize a novel neural cryptography scheme. The key idea is to assume that Alice and Bob share a copy of an echo state network. If Alice trains her copy to memorize a message, she can communicate the trained part of the network to Bob who plugs it into his copy to regenerate the message. Considering a byte-level representation of in- and output, the technique applies to arbitrary types of data (texts, images, audio files, etc.) and practical experiments reveal it to satisfy the fundamental cryptographic properties of diffusion and confusion.