/// <summary>
/// Uses the <see cref="IAgent"/>s produced by an <see cref="IAgentEnvironment"/> to teach a <see cref="NeuralNetwork"/> with the goal of increasing the <see cref="IAgent.GetFitness"/> of resulting <see cref="IAgent"/>s.
/// </summary>
/// <param name="algorithm">The given <see cref="IAgentLearningAlgorithm"/>.</param>
/// <param name="environment">The <see cref="IAgentEnvironment"/> containing the <see cref="IAgent"/>s to train.</param>
/// <param name="generations">The number of generations of <see cref="IAgent"/>s to go through.</param>
public static double[] Teach(this IAgentLearningAlgorithm algorithm, IAgentEnvironment environment, int generations)
{
double[] output = new double[generations];
for (int i = 0; i < generations; i++)
{
output[i] = algorithm.Teach(environment);
}
return(output);
}
private void RunEnvironment(IAgentEnvironment environment)
{
environment.Setup();
for (int i = 0; i < NumberOfExecutes; i++)
{
foreach (var agent in environment.Agents)
{
agent.Execute();
}
}
}
private void Mutate(IAgentEnvironment environment)
{
foreach (var neuron in environment.Agents.SelectMany(
agent => agent.Network.Layers.SelectMany(layer => layer.Neurons)))
{
neuron.Bias += Rand.Current.NextDouble(-MutationLevel, MutationLevel);
foreach (var synapse in neuron.Synapses)
{
synapse.Weight += Rand.Current.NextDouble(-MutationLevel, MutationLevel);
}
}
}
/// <inheritdoc/>
public double Teach(IAgentEnvironment environment)
{
//// Create the specified number of environments.
IAgentEnvironment[] eList = Enumerable.Range(0, GenerationSize).Select(i => environment.Copy()).ToArray();
Parallel.ForEach(eList, e =>
{
Mutate(e);
RunEnvironment(e);
});
var fitnesses = eList.Select(e => e.Agents.Select(a => a.GetFitness())).Transpose()
.Select(t => t.ToList()).ToList();
//// Find the best network for each type of agent in the environment, and swap networks.
for (int i = 0; i < fitnesses.Count; i++)
{
int iMaxIndex = fitnesses[i].IndexOf(fitnesses[i].Max());
environment.Agents.ElementAt(i).Network =
eList[iMaxIndex].Agents.ElementAt(i).Network;
}
return(fitnesses.Average(f => f.Max()));
}
public void SetAgentEnvironment(IAgentEnvironment agentEnviroment)
{
_agentEnvironment = agentEnviroment;
}
