private ForagingAgent getAgent(int i, IBlackBox phenome)
{
switch (AgentType)
{
case AgentTypes.Neural:
return(new NeuralAgent(i, _genomeList[i].SpecieIdx, phenome, _agentsNavigate, _agentsHide));
case AgentTypes.Social:
var a = new SocialAgent(i, _genomeList[i].SpecieIdx, phenome, _agentsNavigate, _agentsHide,
sar => sar.Last.Value.Reward > 0)
{
MemorySize = CurrentMemorySize
};
var network = (FastCyclicNetwork)phenome;
network.Momentum = ((SocialAgent)a).Momentum;
network.BackpropLearningRate = ((SocialAgent)a).LearningRate;
return(a);
case AgentTypes.QLearning:
return(new QLearningAgent(i, _genomeList[i].SpecieIdx, phenome, _agentsNavigate, _agentsHide,
8, 4, _world));
case AgentTypes.Spinning:
return(new SpinningAgent(i));
default:
return(null);
}
}
private void GenomesToAcceptability(IList <TGenome> genomeList)
{
string TEMP_NETWORK_FILE = string.Format("____temp{0}____network.xml", TrialId);
var neatGenomeParams = new NeatGenomeParameters()
{
ActivationFn = PlainSigmoid.__DefaultInstance,
InitialInterconnectionsProportion = 1
};
int inputs = _world.PlantTypes.Count() * World.SENSORS_PER_OBJECT_TYPE
+ _world.Predators.Count() * World.SENSORS_PER_OBJECT_TYPE
+ 1;
int outputs = 2;
var factory = new NeatGenomeFactory(inputs, outputs, neatGenomeParams);
for (int i = 0; i < _agents.Length; i++)
{
// Decode the genome.
IBlackBox phenome = _genomeDecoder.Decode(genomeList[i]);
IAcceptabilityFunction accept = new RecurrentNeuralAcceptability(phenome);
// Check that the genome is valid.
if (phenome == null)
{
Console.WriteLine("Couldn't decode genome {0}!", i);
_agents[i] = new SpinningAgent(i);
continue;
}
// Create a feed forward network with 10 hidden nodes and random weights
SocialExperiment.CreateNetwork(TEMP_NETWORK_FILE, inputs, outputs);
using (var xr = XmlReader.Create(TEMP_NETWORK_FILE))
{
var controllerGenome = NeatGenomeXmlIO.ReadCompleteGenomeList(xr, false, factory)[0];
var controllerPhenome = _genomeDecoder.Decode((TGenome)controllerGenome);
_agents[i] = new SocialAgent(i, _genomeList[i].SpecieIdx, controllerPhenome, _agentsNavigate, _agentsHide, accept)
{
MemorySize = CurrentMemorySize
};
var network = (FastCyclicNetwork)controllerPhenome;
network.Momentum = ((SocialAgent)_agents[i]).Momentum;
network.BackpropLearningRate = ((SocialAgent)_agents[i]).LearningRate;
}
}
File.Delete(TEMP_NETWORK_FILE);
}
// Handle the reward-based teaching paradigms whenever a plant is eaten.
void _world_PlantEaten(object sender, IAgent eaterAgent, Plant eaten)
{
if (!_learningEnabled)
{
return;
}
// if we're not dealing with a social teacher, then skip this notification.
if (!(eaterAgent is SocialAgent))
{
return;
}
// Only learn from rewards if we're using a reward-based social learning paradigm
if (TeachParadigm != TeachingParadigm.EveryoneRewards &&
TeachParadigm != TeachingParadigm.SubcultureRewards &&
TeachParadigm != TeachingParadigm.EveryoneRewardsAndPolling &&
TeachParadigm != TeachingParadigm.SubcultureRewardsAndPolling &&
TeachParadigm != TeachingParadigm.SubcultureRewardFiltering)
{
return;
}
SocialAgent eater = (SocialAgent)eaterAgent;
// Only learn from positive rewards.
//if (eaten.Species.Reward > 0)
//{
// Train all the agents in parallel
for (int i = 0; i < _agents.Length; i++)
{
SocialAgent agent = (SocialAgent)_agents[i];
// Do not try to teach yourself
if (agent == eater)
{
continue;
}
// Only update individuals in your subculture
if ((TeachParadigm == TeachingParadigm.SubcultureRewards ||
TeachParadigm == TeachingParadigm.SubcultureRewardFiltering ||
TeachParadigm == TeachingParadigm.SubcultureRewardsAndPolling) &&
_agentGroups[eater.Id] != _agentGroups[i])
{
continue;
}
// Only learn from high-valued actions
//if (TeachParadigm == TeachingParadigm.SubcultureRewardFiltering
// && eaten.Species.Reward < _rewardThreshold
// && _rewards.Count > 20)
// //return;
// continue;
// Teach the teacher to act like the eater
if (agent.AcceptabilityFn.Accept(eater.Memory))
{
TeachAgent(eater, agent);
}
}
//}
}