void printOptions(options_t &options){
std::cout << "Agent configuration:\n------------------------------\n";
for(options_t::iterator it = options.begin(); it != options.end(); ++it){
std::cout << "OPTION: '" << it->first << "' = '" << it->second << "'" << std::endl;
}
std::cout << std::endl;
}
int OOBase::CmdArgs::parse_short_options(options_t& options, const char** argv, int& arg, int argc) const
{
String strKey,strVal;
for (const char* c = argv[arg]+1; *c!='\0'; ++c)
{
Table<String,Option>::const_iterator i = m_map_opts.begin();
for (;i; ++i)
{
if (i->second.m_short_opt == *c)
{
if (i->second.m_has_value)
{
const char* value;
if (c[1] == '\0')
{
// Next arg is the value
if (arg >= argc-1)
{
int err = strVal.printf("-%c",*c);
if (err)
return err;
return error(options,EINVAL,"missing",strVal.c_str());
}
value = argv[++arg];
}
else
value = &c[1];
if (!strVal.assign(value) || !options.insert(i->first,strVal))
return system_error();
// No more for this arg...
return 0;
}
else
{
if (!strVal.assign("true") || !options.insert(i->first,strVal))
return system_error();
break;
}
}
}
if (!i)
{
int err = strVal.printf("-%c",*c);
if (err)
return err;
return error(options,ENOENT,"unknown",strVal.c_str());
}
}
return 0;
}
int OOBase::CmdArgs::error(options_t& options, int err, const char* key, const char* value) const
{
String strErr,strVal;
if (!strErr.assign(key) || !strVal.assign(value))
return system_error();
options.clear();
if (!options.insert(strErr,strVal))
return system_error();
return err;
}
CoinFlip::CoinFlip(options_t &options) {
// Determine the probability of the coin landing on heads
p = 1.0;
if (options.count("coin-flip-p") > 0) {
strExtract(options["coin-flip-p"], p);
}
assert(0.0 <= p);
assert(p <= 1.0);
// Set up the initial observation
m_observation = rand01() < p ? 1 : 0;
m_reward = 0;
}
int OOBase::CmdArgs::parse_long_option(options_t& options, const char** argv, int& arg, int argc) const
{
String strKey,strVal;
for (Table<String,Option>::const_iterator i = m_map_opts.begin();i; ++i)
{
const char* value = "true";
if (i->second.m_long_opt == argv[arg]+2)
{
if (i->second.m_has_value)
{
if (arg >= argc-1)
return error(options,EINVAL,"missing",argv[arg]);
value = argv[++arg];
}
if (!strVal.assign(value) || !options.insert(i->first,strVal))
return system_error();
return 0;
}
if (strncmp(i->second.m_long_opt.c_str(),argv[arg]+2,i->second.m_long_opt.length())==0 && argv[arg][i->second.m_long_opt.length()+2]=='=')
{
if (i->second.m_has_value)
value = &argv[arg][i->second.m_long_opt.length()+3];
if (!strVal.assign(value) || !options.insert(i->first,strVal))
return system_error();
return 0;
}
}
return error(options,ENOENT,"unknown",argv[arg]);
}
// The main agent/environment interaction loop
void mainLoop(Agent &ai, Environment &env, options_t &options) {
// Determine exploration options
bool explore = options.count("exploration") > 0;
double explore_rate, explore_decay;
if (explore) {
strExtract(options["exploration"], explore_rate);
strExtract(options["explore-decay"], explore_decay);
assert(0.0 <= explore_rate && explore_rate <= 1.0);
assert(0.0 <= explore_decay && explore_decay <= 1.0);
}
// Determine termination age
bool terminate_check = options.count("terminate-age") > 0;
age_t terminate_age;
if (terminate_check) {
strExtract(options["terminate-age"], terminate_age);
assert(0 <= terminate_age);
}
// Determine mc-timelimit
timelimit_t mc_timelimit;
strExtract(options["mc-timelimit"], mc_timelimit);
//if we assume that time_limit > agent.numActions() we can be sure
//that every action is selected at least once
if(mc_timelimit < ai.numActions()){
std::cerr << "WARNING: time_limit not large enough to sample all actions" << std::endl;
}
// Determine whether to write cts during the process, or only at the end.
bool intermediate_ct = true;
if(options.count("intermediate-ct") > 0){
intermediate_ct = !(options["intermediate-ct"] == "0");
}
std::cout << "starting agent/environment interaction loop...\n";
// Agent/environment interaction loop
for (unsigned int cycle = 1; !env.isFinished(); cycle++) {
// check for agent termination
if (terminate_check && ai.age() >= terminate_age) {
verboseLog << "info: terminating agent" << std::endl;
break;
}
// Get a percept from the environment
percept_t observation = env.getObservation();
percept_t reward = env.getReward();
// Update agent's environment model with the new percept
ai.modelUpdate(observation, reward);
// Determine best exploitive action, or explore
action_t action;
bool explored = false;
if (explore && rand01() < explore_rate) {
explored = true;
action = ai.genRandomAction();
}
else {
action = search(ai, mc_timelimit);
}
// Send an action to the environment
env.performAction(action);
// Update agent's environment model with the chosen action
ai.modelUpdate(action);
// Log this turn
verboseLog << "cycle: " << cycle << std::endl;
verboseLog << "observation: " << observation << std::endl;
verboseLog << "reward: " << reward << std::endl;
verboseLog << "action: " << action << std::endl;
verboseLog << "explored: " << (explored ? "yes" : "no") << std::endl;
verboseLog << "explore rate: " << explore_rate << std::endl;
verboseLog << "total reward: " << ai.reward() << std::endl;
verboseLog << "average reward: " << ai.averageReward() << std::endl;
// Log the data in a more compact form
compactLog << cycle << ", " << observation << ", " << reward << ", "
<< action << ", " << explored << ", " << explore_rate << ", "
<< ai.reward() << ", " << ai.averageReward() << std::endl;
// Print to standard output when cycle == 2^n
if ((cycle & (cycle - 1)) == 0) {
std::cout << "cycle: " << cycle << std::endl;
std::cout << "average reward: " << ai.averageReward() << std::endl;
if (explore) {
std::cout << "explore rate: " << explore_rate << std::endl;
}
// Write context tree file
if(options["write-ct"] != "" && intermediate_ct){
// write a ct for each 2^n cycles.
char cycle_string[256];
sprintf(cycle_string, "%d", cycle);
std::ofstream ct((options["write-ct"] + std::string(cycle_string) + ".ct").c_str());
ai.writeCT(ct);
ct.close();
}
}
// Update exploration rate
if (explore) explore_rate *= explore_decay;
}
// Print summary to standard output
std::cout << std::endl << std::endl << "SUMMARY" << std::endl;
std::cout << "agent age: " << ai.age() << std::endl;
std::cout << "average reward: " << ai.averageReward() << std::endl;
// Write context tree file
if(options["write-ct"] != ""){
// write a ct for the final cycle too.
char cycle_string[256];
sprintf(cycle_string, "%lld", ai.age());
std::ofstream ct((options["write-ct"] + std::string(cycle_string) + ".ct").c_str());
ai.writeCT(ct);
ct.close();
}
}
// The main agent/environment interaction loop
void mainLoop(Agent &ai, Environment &env, options_t &options) {
// Determine exploration options
bool explore = options.count("exploration") > 0;
double explore_rate, explore_decay;
if (explore) {
strExtract(options["exploration"], explore_rate);
strExtract(options["explore-decay"], explore_decay);
assert(0.0 <= explore_rate && explore_rate <= 1.0);
assert(0.0 <= explore_decay && explore_decay <= 1.0);
}
// Determine termination lifetime
bool terminate_check = options.count("terminate-lifetime") > 0;
lifetime_t terminate_lifetime;
if (terminate_check) {
strExtract(options["terminate-lifetime"], terminate_lifetime);
assert(0 <= terminate_lifetime);
}
// Agent/environment interaction loop
for (unsigned int cycle = 1; !env.isFinished(); cycle++) {
// check for agent termination
if (terminate_check && ai.lifetime() > terminate_lifetime) {
log << "info: terminating lifetiment" << std::endl;
break;
}
// Get a percept from the environment
percept_t observation = env.getObservation();
percept_t reward = env.getReward();
// Update agent's environment model with the new percept
ai.modelUpdate(observation, reward); // TODO: implement in agent.cpp
// Determine best exploitive action, or explore
action_t action;
bool explored = false;
if (explore && rand01() < explore_rate) {
explored = true;
action = ai.genRandomAction();
}
else {
action = search(ai); // TODO: implement in search.cpp
}
// Send an action to the environment
env.performAction(action); // TODO: implement for each environment
// Update agent's environment model with the chosen action
ai.modelUpdate(action); // TODO: implement in agent.cpp
// Log this turn
log << "cycle: " << cycle << std::endl;
log << "observation: " << observation << std::endl;
log << "reward: " << reward << std::endl;
log << "action: " << action << std::endl;
log << "explored: " << (explored ? "yes" : "no") << std::endl;
log << "explore rate: " << explore_rate << std::endl;
log << "total reward: " << ai.reward() << std::endl;
log << "average reward: " << ai.averageReward() << std::endl;
// Log the data in a more compact form
compactLog << cycle << ", " << observation << ", " << reward << ", "
<< action << ", " << explored << ", " << explore_rate << ", "
<< ai.reward() << ", " << ai.averageReward() << std::endl;
// Print to standard output when cycle == 2^n
if ((cycle & (cycle - 1)) == 0) {
std::cout << "cycle: " << cycle << std::endl;
std::cout << "average reward: " << ai.averageReward() << std::endl;
if (explore) {
std::cout << "explore rate: " << explore_rate << std::endl;
}
}
// Update exploration rate
if (explore) explore_rate *= explore_decay;
}
// Print summary to standard output
std::cout << std::endl << std::endl << "SUMMARY" << std::endl;
std::cout << "agent lifetime: " << ai.lifetime() << std::endl;
std::cout << "average reward: " << ai.averageReward() << std::endl;
}
/** The main agent/environment interaction loop. Each interaction cycle begins
* with the agent receiving an observation and reward from the environment.
* Subsequently, the agent selects an action and informs the environment. The
* interactions that took place are logged to the ::logger and ::compactLogger
* streams. When the cycle equals a power of two, a summary of the interactions
* is printed to the standard output.
* \param ai The agent.
* \param env The environment.
* \param options The configuration options. */
void mainLoop(Agent &ai, Environment &env, options_t &options) {
// Apply random seed (Defaut: 0)
srand(getOption<unsigned int>(options, "random-seed", 0));
// Verbose output (Default: false)
bool verbose = getOption<bool>(options, "verbose", false);
// Determine exploration options (Default: don't explore, don't decay)
bool explore = options.count("exploration") > 0;
double explore_rate = getOption<double>(options, "exploration", 0.0);
double explore_decay = getOption<double>(options, "explore-decay", 1.0);
assert(0.0 <= explore_rate);
assert(0.0 <= explore_decay && explore_decay <= 1.0);
// Determine termination age (Default: don't terminate)
bool terminate_check = options.count("terminate-age") > 0;
age_t terminate_age = getOption<age_t>(options, "terminate-age", 0);
assert(0 <= terminate_age);
// Determine the cycle after which the agent stops learning (if ever)
int learning_period = getOption<int>(options, "learning-period", 0);
assert(0 <= learning_period);
// Agent/environment interaction loop
for (int cycle = 1; !env.isFinished(); cycle++) {
// Check for agent termination
if (terminate_check && ai.age() > terminate_age) {
break;
}
// Save the current clock cycle (to compute how long this cycle took)
clock_t cycle_start = clock();
// Get a percept from the environment
percept_t observation = env.getObservation();
percept_t reward = env.getReward();
if (learning_period > 0 && cycle > learning_period)
explore = false;
// Update agent's environment model with the new percept
ai.modelUpdate(observation, reward);
// Determine best exploitive action, or explore
action_t action;
bool explored = false;
if (explore && (rand01() < explore_rate)) { // Explore
explored = true;
action = ai.genRandomAction();
}
else { // Exploit
action = ai.search();
}
// Send an action to the environment
env.performAction(action);
// Update agent's environment model with the chosen action
ai.modelUpdate(action);
// Calculate how long this cycle took
double time = double(clock() - cycle_start) / double(CLOCKS_PER_SEC);
// Log this turn
logger << cycle << ", " << observation << ", " << reward << ", "
<< action << ", " << explored << ", " << explore_rate << ", "
<< ai.totalReward() << ", " << ai.averageReward() << ", "
<< time << ", " << ai.modelSize() << std::endl;
// Print to standard output when cycle == 2^n or on verbose option
if (verbose || (cycle & (cycle - 1)) == 0) {
std::cout << "cycle: " << cycle << std::endl;
std::cout << "average reward: " << ai.averageReward() << std::endl;
if (explore) {
std::cout << "explore rate: " << explore_rate << std::endl;
}
}
// Print environment state if verbose option is true
if (verbose) {
std::cout << env.print();
}
// Update exploration rate
if (explore) explore_rate *= explore_decay;
}
// Print summary to standard output
std::cout << std::endl << std::endl << "SUMMARY" << std::endl;
std::cout << "agent age: " << ai.age() << std::endl;
std::cout << "average reward: " << ai.averageReward() << std::endl;
}
void argparse::init_options(options_t& options) {
options.reset();
}
int argparse::parse_arguments(char* arg, options_t& options) {
std::string argument = arg, option, value;
if (argument.compare(0, 8, "--macpo:") != 0)
return -1;
size_t start_position = -1, end_position;
if ((start_position = argument.find(":")) == std::string::npos)
return -1;
end_position = argument.find("=", start_position + 1);
if (end_position == std::string::npos) {
option = argument.substr(start_position + 1);
value = "";
} else {
option = argument.substr(start_position + 1,
end_position - start_position - 1);
value = argument.substr(end_position + 1);
}
location_t location;
if (option == "instrument") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_INSTRUMENT, location);
} else if (option == "check-alignment") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_ALIGNCHECK, location);
} else if (option == "record-tripcount") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_TRIPCOUNT, location);
} else if (option == "record-branchpath") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_BRANCHPATH, location);
} else if (option == "gen-trace") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_GENTRACE, location);
} else if (option == "vector-strides") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_VECTORSTRIDES, location);
} else if (option == "overlap-check") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_OVERLAPCHECK, location);
} else if (option == "stride-check") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_STRIDECHECK, location);
} else if (option == "reuse-distance") {
if (!value.size())
return -1;
parse_location(value, location);
options.add_location(ACTION_REUSEDISTANCE, location);
} else if (option == "backup-filename") {
if (!value.size())
return -1;
options.backup_filename = value;
} else if (option == "no-compile") {
options.no_compile = true;
} else if (option == "enable-sampling") {
options.disable_sampling = false;
} else if (option == "disable-sampling") {
options.disable_sampling = true;
} else if (option == "profile-analysis") {
options.profile_analysis = true;
} else if (option == "compiler") {
// Check if we were passed a valid executable.
if (!value.size()) {
return -1;
}
options.base_compiler = value;
} else {
return -1;
}
return 0;
}