/// Refines this model based on a recently performed action and change in beliefs
void TransitionModel::trainIncremental(const GVec& beliefs, const GVec& actions, const GVec& nextBeliefs)
{
// Buffer the pattern
GVec& destIn = trainInput.row(trainPos);
GVec& destOut = trainOutput.row(trainPos);
trainPos++;
trainSize = std::max(trainSize, trainPos);
if(trainPos >= trainInput.rows())
trainPos = 0;
if(beliefs.size() + actions.size() != destIn.size() || beliefs.size() != destOut.size())
throw Ex("size mismatch");
destIn.put(0, beliefs);
destIn.put(beliefs.size(), actions);
for(size_t i = 0; i < destOut.size(); i++)
destOut[i] = 0.5 * (nextBeliefs[i] - beliefs[i]);
/*
destIn.print();
std::cout << "->";
destOut.print();
std::cout << "\n";
std::cout << to_str(0.5 * cos(destIn[2])) << ", " << to_str(0.5 * sin(destIn[2])) << "\n";
*/
// Refine the model
size_t iters = std::min(trainIters, 1000 * trainSize);
for(size_t i = 0; i < iters; i++)
doSomeTraining();
}
/// Predict the belief vector that will result if the specified action is performed
void TransitionModel::anticipateNextBeliefs(const GVec& beliefs, const GVec& actions, GVec& anticipatedBeliefs)
{
if(tutor)
tutor->transition(beliefs, actions, anticipatedBeliefs);
else
{
GAssert(beliefs.size() + actions.size() == model.layer(0).inputs());
buf.resize(beliefs.size() + actions.size());
buf.put(0, beliefs);
buf.put(beliefs.size(), actions);
model.forwardProp(buf);
anticipatedBeliefs.copy(beliefs);
anticipatedBeliefs.addScaled(2.0, model.outputLayer().activation());
anticipatedBeliefs.clip(-1.0, 1.0);
}
}
/// Encodes observations to predict beliefs
void ObservationModel::observationsToBeliefs(const GVec& observations, GVec& beliefs)
{
beliefs.resize(encoder.outputLayer().outputs());
if(tutor)
tutor->observations_to_state(observations, beliefs);
else
{
beliefs.put(0, observations, 0, beliefs.size());
encoder.forwardProp(observations);
beliefs.copy(encoder.outputLayer().activation());
}
}
/// Finds the best plan and copies its first step
void PlanningSystem::chooseNextActions(const GVec& beliefs, GVec& actions)
{
if(tutor)
tutor->choose_actions(beliefs, actions);
else
{
// Find the best plan (according to the contentment model) and ask the mentor to evaluate it
size_t planBestIndex = 0;
double bestContentment = -1e300;
for(size_t i = 0; i < plans.size(); i++)
{
double d = evaluatePlan(beliefs, *plans[i]);
if(d > bestContentment)
{
bestContentment = d;
planBestIndex = i;
}
}
//std::cout << "Best contentment: " << to_str(bestContentment) << "\n";
GMatrix& bestPlan = *plans[planBestIndex];
askMentorToEvaluatePlan(beliefs, bestPlan);
// Pick a random plan from the population and ask the mentor to evaluate it (for contrast)
size_t planBindex = rand.next(plans.size() - 1);
if(planBindex >= planBestIndex)
planBindex++;
askMentorToEvaluatePlan(beliefs, *plans[planBindex]);
// Make a random one-step plan, and ask the mentor to evaluate it (for contrast)
GVec& action = randomPlan[0];
action.fillUniform(rand);
askMentorToEvaluatePlan(beliefs, randomPlan);
// Copy the first action vector of the best plan for our chosen action
GVec* bestActions = &bestPlan[0];
if(burnIn > 0 || rand.uniform() < explorationRate)
bestActions = &randomPlan[0];
if(burnIn > 0)
burnIn--;
GAssert(bestActions->size() == actionDims);
actions.copy(*bestActions);
}
}