double normRand(double mean, double sigma)
{
// Create a Mersenne twister random number generator
// that is seeded once with #seconds since 1970
static boost::mt19937 rng(static_cast<unsigned> (std::time(0)));
// select Gaussian probability distribution
boost::normal_distribution<double> norm_dist(mean, sigma);
// bind random number generator to distribution, forming a function
boost::variate_generator<boost::mt19937&, boost::normal_distribution<double> > normal_sampler(rng, norm_dist);
// sample from the distribution
return normal_sampler();
}
int SampleNormal (double mean, double sigma, int seed)
{
// Create a Mersenne twister random number generator
// that is seeded once with #seconds since 1970
static mt19937 rng(static_cast<unsigned> (seed));
// select Gaussian probability distribution
normal_distribution<double> norm_dist(mean, sigma);
// bind random number generator to distribution, forming a function
variate_generator<mt19937&, normal_distribution<double> > normal_sampler(rng, norm_dist);
// sample from the distribution
return (int) normal_sampler();
}
void new_episode()
{
numActions = 0;
numEpisodes += 1;
if (numEpisodes > 300)
normal_sampler = normal_variate_generator( rng, boost::normal_distribution<ValueType>(0, value_type(0.1)) );
environment.query_state( lastState.begin() );
lastValue = valueFunction.compute( lastState.begin(),
lastState.end() );
// select first action
bestAction.compute( lastState.begin(),
lastState.end(),
lastAction.begin() );
for (size_t i = 0; i < actionSize; ++i)
{
//lastAction[i] += (*normal_sampler[i])();
lastAction[i] += normal_sampler();
}
lastReward = environment.perform_action( lastAction.begin(),
lastAction.end() );
eligibility = vector_type(eligibility.size());
std::fill(eligibility.begin(), eligibility.end(), value_type());
}
bool update()
{
bool terminal = environment.query_state( currentState.begin() );
if (terminal)
{
// V_{t+1}(s_t) <- r_t
valueFunction.update( lastState.begin(),
lastState.end(),
lastReward,
beta );
return true;
}
else // nonterminal state
{
// V_{t+1}(s_t) <- r_t + gamma*V_t(s_{t+1})
ValueType delta = lastReward
+ gamma * valueFunction.compute( currentState.begin(), currentState.end() )
- lastValue;
/*valueFunction.update( lastState.begin(),
lastState.end(),
value,
beta );
value = valueFunction.compute( lastState.begin(), lastState.end() );*/
vector_type gradient(valueFunction.num_params());
valueFunction.get_gradient(lastState.begin(), lastState.end(), gradient.begin());
eligibility = gamma*lambda*eligibility + gradient;
vector_type params(valueFunction.num_params());
valueFunction.get_params(params.begin());
params += beta*delta*eligibility;
valueFunction.set_params(params.begin(), params.end());
ValueType value = valueFunction.compute( lastState.begin(), lastState.end() );
if (value > lastValue)
{
// update best action
bestAction.update( lastState.begin(),
lastState.end(),
lastAction.begin(),
lastAction.end(),
alpha );
}
lastValue = valueFunction.compute( currentState.begin(), currentState.end() );
}
// select new action
bestAction.compute( currentState.begin(),
currentState.end(),
lastAction.begin() );
// sum with variation
for (size_t i = 0; i < actionSize; ++i)
{
//lastAction[i] += (*normal_sampler[i])();
lastAction[i] += normal_sampler();
}
value_type distance = value_type();
for (size_t i = 0; i<currentState.size(); ++i) {
distance += sqrt( (currentState[i] - lastState[i]) * (currentState[i] - lastState[i]) );
}
if (distance < 0.1)
{
std::cerr << "random action" << std::endl;
for (size_t i = 0; i < actionSize; ++i)
{
//lastAction[i] += (*normal_sampler[i])();
lastAction[i] = uniform_sampler();
}
}
averaged_reward = averaged_reward*gamma + lastReward;
//fileStat << averaged_reward << std::endl;
lastState.swap(currentState);
lastReward = environment.perform_action( lastAction.begin(),
lastAction.end() );
++numActions;
if (numActions == 20000)
{
normal_sampler = normal_variate_generator( rng, boost::normal_distribution<ValueType>(0, value_type(0.05)) );
}
return false;
}
