Bohdana Ratitch

Learn More
In this paper, we advocate the use of Sparse Distributed Memories (SDMs) for on-line, value-based reinforcement learning (RL). SDMs provide a linear, local function approximation scheme, designed to work when a very large/ high-dimensional input (address) space has to be mapped into a much smaller physical memory. We present an implementation of the SDM(More)
Patrolling tasks can be encountered in a variety of real-world domains, ranging from computer network administration and surveillance to computer wargame simulations. It is a complex multi-agent task, which usually requires agents to coordinate their decision-making in order to achieve optimal performance of the group as a whole. In this paper, we show how(More)
In this paper, we advocate the use of Sparse Distributed Memories (SDMs) (Kanerva, 1993) for on-line, value-based reinforcement learning (RL). The SDMs model was originally designed for the case, where a very large input (address) space has to be mapped into a much smaller physical memory. SDMs provide a linear, local function approximation scheme, which is(More)
Patrolling tasks can be encountered in a variety of real-world domains, ranging from computer network administration and surveillance to computer wargame simulations. It is a complex multi-agent task, which usually requires agents to coordinate their decision-making in order to achieve optimal performance of the group as a whole. In this paper, we show how(More)
  • 1