Proposal cs221 project: Sheepherding

Sheephearding is one of the oldest trades, where a herder (with help of one or multiple dogs) tries to bring sheep together at some target location. In this project, we define a simplified model of sheepherding where dogs try to move sheep to a target. We then use simulations and apply reinforcement learning to train an AI for the dogs that is capable of performing several tasks related to sheepherding. In particular, we are interested in cooperative behavior: can dogs cooperate efficiently without communication, or does a central authority that commands to the dogs much better at herding?

First, we give a brief overview of some related literature. In \cite{Vaughan98robotsheepdog}, a method is proposed to gather ducklings using a robot in a simple circular area, but their approach is not based on any learning. \cite{ai_for_herding_sheep} does discuss methods for herging sheep from an AI perspective, where they use a hierarchical and stack-based finite state machine. \cite{Schultz96robo-shepherd:learning} proposes to use genetic algorithms to fing decision rules to model robots behavior. In a case study, one robot tries to guide another robot to a target location. An interesting perspective is given in \cite{potter2001heterogeneity}, where so-called specialists are discussed. Sometimes it is more useful for agents to specify in specific tasks (such as in playing soccer), while for other tasks this is not useful. Finally, \cite{Utile:} discusses an approach where agents start by acting individually, but learn how and when to cooperate if needed. This, they argue, leads to a small state space because only necessaray interaction is taken into account.

Underlying all simulations is a physical model that describes the world, and the movement of sheep in particular.

The world is modeled as a box, \([0, x_{m}]\times[0,y_{m}]\). We then represent the sheep, dogs and target(s) as points in this space. The majority of the literature uses a grid to model the world, but we choose not to discretize the space, since that allows for more prolific movement, a more realistic environment, and a more interesting modeling perspective. Sometimes, people add other agents or obstacles to the world, but we propose to leave those out to start with.

Here, we get our inspiration from Boids (Reynolds 1987), and define the following axioms for movement of sheep:

- Axiom 1
Sheep move away from dogs when sufficiently close.

- Axion 2
When two sheep are very close to each other, they want more space and move away.

- Axiom 3
When two sheep are neither too close, nor too far, they flock together.

We translate these axioms into forces that are acting on the sheep. These forces then drive the sheep in a certain direction and define the speed at which the sheep moves. We note that others use similar approaches.

All forces are calculated using the distance between the sheep and the other object, \(z\), denoted by \(d_z\), and are of the following form: \[F_{a,b}(d_z) = b\left(1-\sqrt{\frac{d_z}{a}}\right)_+\] and where the direction of the force is given by the angle of \(z\) relative to the sheep. Note that the force function is paramaterized by \(a\) and \(b\)

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