/**
* 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;
}
int main(int argc, char *argv[]) {
const char* task_spec;
const reward_observation_action_terminal_t *stepResponse;
const observation_action_t *startResponse;
printf("\n\nExperiment starting up!\n");
task_spec=RL_init();
printf("RL_init called, the environment sent task spec: %s\n",task_spec);
// RL_env_message and RL_agent_message may be used to communicate with the environment
// and agent, respectively. See RL-Glue documentation for details.
// const char* responseMessage;
// responseMessage=RL_agent_message("what is your name?");
printf("\n\n----------Running a few episodes----------\n");
// Use the RL-Glue-provided RL_episode to run a few episodes of ALE.
// 0 means no limit at all.
runEpisode(10000);
runEpisode(0);
runEpisode(0);
runEpisode(0);
runEpisode(0);
RL_cleanup();
printf("\n\n----------Stepping through an episode----------\n");
// The following demonstrates how to step through an episode.
task_spec=RL_init();
// Start the episode
startResponse=RL_start();
printf("First action was: %d\n", startResponse->action->intArray[0]);
// Run one step
stepResponse=RL_step();
// Run until end of episode
while(stepResponse->terminal != 1) {
stepResponse=RL_step();
}
// Demonstrates other RL-Glue functionality.
printf("It ran for %d steps, total reward was: %f\n",RL_num_steps(), RL_return());
RL_cleanup();
return 0;
}
// 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++;
}
int main(int argc, char *argv[]) {
const char* task_spec;
const char* responseMessage;
const reward_observation_action_terminal_t *stepResponse;
const observation_action_t *startResponse;
printf("\n\nExperiment starting up!\n");
task_spec=RL_init();
printf("RL_init called, the environment sent task spec: %s\n",task_spec);
printf("\n\n----------Sending some sample messages----------\n");
/*Talk to the agent and environment a bit...*/
responseMessage=RL_agent_message("what is your name?");
printf("Agent responded to \"what is your name?\" with: %s\n",responseMessage);
responseMessage=RL_agent_message("If at first you don't succeed; call it version 1.0");
printf("Agent responded to \"If at first you don't succeed; call it version 1.0\" with: %s\n\n",responseMessage);
responseMessage=RL_env_message("what is your name?");
printf("Environment responded to \"what is your name?\" with: %s\n",responseMessage);
responseMessage=RL_env_message("If at first you don't succeed; call it version 1.0");
printf("Environment responded to \"If at first you don't succeed; call it version 1.0\" with: %s\n",responseMessage);
printf("\n\n----------Running a few episodes----------\n");
runEpisode(100);
runEpisode(100);
runEpisode(100);
runEpisode(100);
runEpisode(100);
runEpisode(1);
/* Remember that stepLimit of 0 means there is no limit at all!*/
runEpisode(0);
RL_cleanup();
printf("\n\n----------Stepping through an episode----------\n");
/*We could also start over and do another experiment */
task_spec=RL_init();
/*We could run one step at a time instead of one episode at a time */
/*Start the episode */
startResponse=RL_start();
printf("First observation and action were: %d %d\n",startResponse->observation->intArray[0],startResponse->action->intArray[0]);
/*Run one step */
stepResponse=RL_step();
/*Run until the episode ends*/
while(stepResponse->terminal!=1) {
stepResponse=RL_step();
if(stepResponse->terminal!=1) {
/*Could optionally print state,action pairs */
/*printf("(%d,%d) ",stepResponse.o.intArray[0],stepResponse.a.intArray[0]);*/
}
}
printf("\n\n----------Summary----------\n");
printf("It ran for %d steps, total reward was: %f\n",RL_num_steps(),RL_return());
RL_cleanup();
return 0;
}
