public World(DeepQLearn brain, int canvas_Width, int canvas_Height)
{
this.agents = new List <Agent>();
this.W = canvas_Width;
this.H = canvas_Height;
this.util = new Util();
this.clock = 0;
// set up walls in the world
this.walls = new List <Wall>();
var pad = 10;
util_add_box(this.walls, pad, pad, this.W - pad * 2, this.H - pad * 2);
util_add_box(this.walls, 100, 100, 200, 300); // inner walls
this.walls.RemoveAt(walls.Count - 1);
util_add_box(this.walls, 400, 100, 200, 300);
this.walls.RemoveAt(walls.Count - 1);
// set up food and poison
this.items = new List <Item>();
for (var k = 0; k < 30; k++)
{
var x = util.randf(20, this.W - 20);
var y = util.randf(20, this.H - 20);
var t = util.randi(1, 3); // food or poison (1 and 2)
var it = new Item(x, y, t);
this.items.Add(it);
}
// set up food and poison
this.agents = new List <Agent>();
this.agents.Add(new Agent(brain));
}
public Agent(DeepQLearn brain)
{
this.brain = brain;
// positional information
this.p = new Vec(50, 50);
this.op = this.p; // old position
this.angle = 0; // direction facing
this.actions = new List <double[]>();
this.actions.Add(new double[] { 1, 1 });
this.actions.Add(new double[] { 0.8, 1 });
this.actions.Add(new double[] { 1, 0.8 });
this.actions.Add(new double[] { 0.5, 0 });
this.actions.Add(new double[] { 0, 0.5 });
// properties
this.rad = 10;
this.eyes = new List <Eye>();
for (var k = 0; k < 9; k++)
{
this.eyes.Add(new Eye((k - 3) * 0.25));
}
this.reward_bonus = 0.0;
this.digestion_signal = 0.0;
// outputs on world
this.rot1 = 0.0; // rotation speed of 1st wheel
this.rot2 = 0.0; // rotation speed of 2nd wheel
this.prevactionix = -1;
}
public World(DeepQLearn brain, int canvas_Width, int canvas_Height, int num_items, bool random, bool obstruct, bool infinite)
{
this.agents = new List <Agent>();
this.W = canvas_Width;
this.H = canvas_Height;
this.num_items = num_items;
this.util = new Util();
this.clock = 0;
// set up walls in the world
this.walls = new List <Wall>();
var pad = 10;
//outer walls
util_add_box(this.walls, pad, pad, this.W - pad * 2, this.H - pad * 2);
// inner walls
if (obstruct)
{
util_add_box(this.walls, 100, 100, 200, 300);
this.walls.RemoveAt(walls.Count - 1);
util_add_box(this.walls, 400, 100, 200, 300);
this.walls.RemoveAt(walls.Count - 1);
}
// set up food/fail and poison/pass test cases
this.items = new List <Item>();
for (var k = 0; k < num_items; k++)
{
double x = 0, y = 0;
int t = 0;
if (random)
{
// define random based objects
x = util.randf(20, this.W - 20);
y = util.randf(20, this.H - 20);
t = util.randi(1, 3); // food/fail or poison/pass (1 and 2)
}
else
{
// define policy based objects
x = (this.W / 35) * (k + 1) + ((k % 2 == 0) ? 50 : -50);
y = (this.H / 35) * (k + 1);
t = (k % 2 == 0) ? 1 : 2; // food/fail or poison/pass (1 and 2)
}
// add objects to environment
var it = new Item(x, y, t);
this.items.Add(it);
}
// set up food and poison
this.agents = new List <Agent>();
this.agents.Add(new Agent(brain));
}
public Agent(DeepQLearn brain)
{
this.brain = brain;
// positional information
//this.position = new Vec(50, 50);
//this.oldPosition = this.position; // old position
this.angle = 0; // direction facing
this.actions = new List <int>();
this.actions.Add(SuperHexagon.STOP);
this.actions.Add(SuperHexagon.CLOCKWISE);
this.actions.Add(SuperHexagon.COUNTERCLOCKWISE);
//this.actions.Add(new double[] { 1, 1 });
//this.actions.Add(new double[] { 0.8, 1 });
//this.actions.Add(new double[] { 1, 0.8 });
//this.actions.Add(new double[] { 0.5, 0 });
//this.actions.Add(new double[] { 0, 0.5 });
// properties
this.rad = 10;
this.eyes = new List <Eye>();
//for (var k = 0; k < 9; k++) { this.eyes.Add(new Eye((k - 3) * 0.25)); }
for (int k = 0; k < 6 /*maxSlots*/; k++)
{
this.eyes.Add(new Eye(k));
}
this.reward_bonus = 0.0;
this.digestion_signal = 0.0;
//// outputs on world
//this.rot1 = 0.0; // rotation speed of 1st wheel
//this.rot2 = 0.0; // rotation speed of 2nd wheel
this.prevactionix = -1;
}
public QAgent(DeepQLearn brain, int canvas_W, int canvas_H)
{
this.w = new World(brain, canvas_W, canvas_H);
}
//[NonSerialized]
//Pen greenPen = new Pen(Color.LightGreen, 2);
//[NonSerialized]
//Pen redPen = new Pen(Color.Red, 2);
//[NonSerialized]
//Pen greenPen2 = new Pen(Color.LightGreen, 1);
//[NonSerialized]
//Pen redPen2 = new Pen(Color.Red, 1);
//[NonSerialized]
//Pen bluePen = new Pen(Color.Blue, 2);
//[NonSerialized]
//Pen blackPen = new Pen(Color.Black);
public QAgent(DeepQLearn brain /*, int canvas_W, int canvas_H*/)
{
this.w = new World(brain /*, canvas_W, canvas_H*/);
}
public QAgent(DeepQLearn brain, int canvas_W, int canvas_H, int num_items, bool random, bool obstruct, bool infinite)
{
this.random = random;
this.infinite = infinite;
this.w = new World(brain, canvas_W, canvas_H, num_items, random, obstruct, infinite);
}
