int main(int argc, char *argv[]) {
const char* task_spec;
long t0,t1;
int steps;
task_spec=RL_init();
//Run an episode to get the System warmed up, etc
RL_episode(500);
t0=get_current_ms_time();
RL_episode(0);
t1=get_current_ms_time();
steps=RL_num_steps();
printf ("\telapsed time in ms: %ld, per step is %f\n", (t1 - t0), ((float)(t1-t0)/(float)steps));
t0=get_current_ms_time();
RL_episode(0);
t1=get_current_ms_time();
steps=RL_num_steps();
printf ("\telapsed time in ms: %ld, per step is %f\n", (t1 - t0), ((float)(t1-t0)/(float)steps));
RL_cleanup();
if(tests_failed!=0)
printf("Failed %d / %d checks in %s\n",tests_failed,test_count, __FILE__);
else
printf("Passed all %d checks in %s\n",test_count,__FILE__);
return tests_failed;
}
/**
* Tell the agent to stop learning, then execute n episodes with his current
* policy. Estimate the mean and variance of the return over these episodes.
*/
evaluation_point_t *evaluate_agent(){
int i=0;
double sum=0;
double sum_of_squares=0;
double this_return=0;
double mean;
double variance;
int n=10;
evaluation_point_t *eval_point=0;
RL_agent_message("freeze learning");
for(i=0;i<n;i++){
/* We use a cutoff here in case the policy is bad
and will never end an episode */
RL_episode(5000);
this_return=RL_return();
sum+=this_return;
sum_of_squares+=this_return*this_return;
}
mean=sum/(double)n;
variance = (sum_of_squares - (double)n*mean*mean)/((double)n - 1.0f);
eval_point=(evaluation_point_t *)malloc(sizeof(evaluation_point_t));
eval_point->mean=mean;
eval_point->standard_dev=sqrt(variance);
RL_agent_message("unfreeze learning");
return eval_point;
}
/*
This function will freeze the agent's policy and test it after every 25 episodes.
*/
void offline_demo(){
int i=0;
int j=0;
evaluation_point_t *this_score=0;
evaluation_point_t *statistics[21];
this_score=evaluate_agent();
print_score(0,this_score);
statistics[0]=this_score;
for(i=0;i<20;i++){
for(j=0;j<25;j++){
RL_episode(0);
}
this_score=evaluate_agent();
print_score((i+1)*25,this_score);
statistics[i+1]=this_score;
}
save_result_csv(statistics,"results.csv");
for(i=0;i<21;i++){
free(statistics[i]);
}
}
int main(int argc, char *argv[]) {
RL_init();
/* No cutoff */
int isTerminal = RL_episode(0);
check_fail(isTerminal!=1);
check_fail(RL_num_steps()!=5);
isTerminal = RL_episode(1);
check_fail(isTerminal!=0);
check_fail(RL_num_steps()!=1);
isTerminal = RL_episode(2);
check_fail(isTerminal!=0);
check_fail(RL_num_steps()!=2);
isTerminal = RL_episode(4);
check_fail(isTerminal!=0);
check_fail(RL_num_steps()!=4);
isTerminal = RL_episode(5);
check_fail(isTerminal!=0);
check_fail(RL_num_steps()!=5);
isTerminal = RL_episode(6);
check_fail(isTerminal!=1);
check_fail(RL_num_steps()!=5);
isTerminal = RL_episode(7);
check_fail(isTerminal!=1);
check_fail(RL_num_steps()!=5);
RL_cleanup();
if(tests_failed!=0)
printf("Failed %d / %d checks in %s\n",tests_failed,test_count, __FILE__);
else
printf("Passed all %d checks in %s\n",test_count,__FILE__);
return tests_failed;
}
// This uses RL-Glue to run a single episode.
void runEpisode(int stepLimit) {
int terminal=RL_episode(stepLimit);
printf("Episode %d\t %d steps \t%f total reward\t %d natural end \n",whichEpisode,RL_num_steps(),RL_return(), terminal);
whichEpisode++;
}
