private IEnumerator Start()
{
var settings = new DQNInitSettings()
{
};
double[] stateArr = new double[] { center.x - transform.position.x, center.y - transform.position.y };
var agent = new DQNAgent(settings, stateArr.Length, 4);
while (true)
{
int action = agent.Act(stateArr);
Vector2 movementVec = default;
switch (action)
{
case 0:
movementVec = new Vector2(1f, 0f) * Time.deltaTime * speed;
break;
case 1:
movementVec = new Vector2(-1f, 0f) * Time.deltaTime * speed;
break;
case 2:
movementVec = new Vector2(0f, 1f) * Time.deltaTime * speed;
break;
case 3:
movementVec = new Vector2(0f, -1f) * Time.deltaTime * speed;
break;
}
Vector2 pos = new Vector2(transform.position.x, transform.position.y);
if ((center - pos).sqrMagnitude > (center - pos + movementVec).sqrMagnitude)
{
Debug.Log("negative");
agent.Learn(-1.0);
}
else
{
Debug.Log("positive");
agent.Learn(1.0);
}
rgb.velocity = movementVec;
yield return(new WaitForFixedUpdate());
}
}
public DQNAgent(DQNInitSettings opt, int numStates, int numActions)
{
this.numStates = numStates;
this.numActions = numActions;
gamma = opt.gamma;
epsilon = opt.epsilon;
alpha = opt.alpha;
experience_add_every = opt.experience_add_every;
experience_size = opt.experience_size;
learning_steps_per_iteration = opt.learning_steps_per_iteration;
tderror_clamp = opt.tderror_clamp;
num_hidden_units = opt.num_hidden_units;
Reset();
}
