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testappornn.cpp
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/*
* testappornn.cpp
*
* Created on: Mar 6, 2022
* Author: zf
*/
#include "alg/rnn/appo/rnnappodataq.h"
#include "alg/rnn/appo/rnnappoupdater.hpp"
#include "alg/rnn/appo/rnnappoworker.hpp"
#include "alg/rnn/appo/rnnappocoupdater.hpp"
#include "alg/utils/algrnntester.hpp"
#include "gymtest/env/airenv.h"
#include "gymtest/env/lunarenv.h"
#include "gymtest/rnnnets/lunarnets/cartacgrutruncnet.h"
#include "gymtest/rnnnets/airnets/airacgrunet.h"
#include "gymtest/rnnnets/airnets/airacgrunordnet.h"
#include "gymtest/train/softmaxpolicy.h"
#include "alg/utils/dqnoption.h"
#include <iostream>
#include <string>
#include <memory>
#include <thread>
#include <torch/torch.h>
#include <log4cxx/logger.h>
#include <log4cxx/basicconfigurator.h>
#include <log4cxx/consoleappender.h>
#include <log4cxx/simplelayout.h>
#include <log4cxx/logmanager.h>
namespace {
log4cxx::LoggerPtr logger(log4cxx::Logger::getLogger("testappornn"));
const torch::Device deviceType = torch::kCUDA;
void testCart(const int clientNum, const int batchSize, const int roundNum, const int maxStep, const int epochNum, const int updateNum) {
//Env Config
const int inputNum = 4;
const int outputNum = 2;
const int hiddenNum = 256;
const int qCapacity = batchSize * roundNum * 2;
// const int maxStep = 8;
const std::string envName = "CartPole-v0";
//Training Config
// const int roundNum = 1;
// const int maxStep = 5;
const int workerNum = roundNum;
// std::vector<float> entropyCoefs {0.01, 0.01, 0.005, 0.02, 0,02};
// assert(workerNum <= entropyCoefs.size());
const int basePort = 10201;
const int testPort = 10210;
const std::string addrBase = "tcp://127.0.0.1:";
const std::string logBase = "./logs/appornn_testcart_r2e2m16/";
const std::string logFileName = "tfevents.pb";
//Net
CartACGRUTruncFcNet model(inputNum, hiddenNum, outputNum);
model.to(deviceType);
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
SoftmaxPolicy policy(outputNum);
std::mutex updateMutex;
//Queue
AsyncRnnPPODataQ q(qCapacity);
//Workers
at::IntArrayRef inputShape{4};
std::vector<LunarEnv*> envs;
std::vector<RnnAPPOWorker<CartACGRUTruncFcNet, LunarEnv, SoftmaxPolicy>*> workers;
for(int i = 0; i < workerNum; i ++) {
DqnOption option(inputShape, deviceType);
//env
option.envNum = clientNum;
option.isAtari = false;
option.donePerEp = 1;
option.multiLifes = false;
//grad
option.gamma = 0.99;
option.ppoLambda = 0.95;
//input
option.inputScale = 1;
option.rewardScale = 1;
option.rewardMin = -1;
option.rewardMax = 1;
//log
option.logInterval = 100;
option.tensorboardLogPath = logBase + std::to_string(i) + "/" + logFileName;
//model
option.saveModel = false;
option.loadModel = false;
//rnn
option.hiddenNums = {hiddenNum};
option.hidenLayerNums = {1};
option.gruCellNum = 1;
option.maxStep = 8;
LOG4CXX_INFO(logger, "To put log in" << option.tensorboardLogPath);
std::string serverAddr = addrBase + std::to_string(basePort + i);
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
LunarEnv* env = new LunarEnv(serverAddr, envName, clientNum);
env->init();
envs.push_back(env);
LOG4CXX_INFO(logger, "Env " << envName << " " << i << " ready");
RnnAPPOWorker<CartACGRUTruncFcNet, LunarEnv, SoftmaxPolicy>* worker =
new RnnAPPOWorker<CartACGRUTruncFcNet, LunarEnv, SoftmaxPolicy> (
model,
*env,
policy,
option,
q,
outputNum
);
workers.push_back(worker);
}
std::vector<std::unique_ptr<std::thread>> ts;
for (int i = 0; i < workerNum; i ++) {
ts.push_back(std::make_unique<std::thread>(
&RnnAPPOWorker<CartACGRUTruncFcNet, LunarEnv, SoftmaxPolicy>::train, &(*workers[i]), updateNum));
}
////////////////////////////////////// Updater ////////////////////////////////////////
at::IntArrayRef updateInputShape{4};
DqnOption updateOption(updateInputShape, deviceType);
//env
updateOption.isAtari = false;
//grad
updateOption.entropyCoef = 0.01;
updateOption.valueCoef = 0.5;
updateOption.maxGradNormClip = 0.5;
updateOption.gamma = 0.99;
//log
updateOption.logInterval = 100;
updateOption.tensorboardLogPath = logBase + "update" + "/" + logFileName;
//input
updateOption.inputScale = 1;
updateOption.rewardScale = 1;
updateOption.rewardMin = -1;
updateOption.rewardMax = 1;
//ppo
updateOption.ppoLambda = 0.95;
updateOption.ppoEpsilon = 0.1;
updateOption.appoRoundNum = roundNum;
updateOption.epochNum = epochNum;
updateOption.batchSize = batchSize;
updateOption.trajStepNum = batchSize * roundNum;
updateOption.maxKl = 0.1;
//test
updateOption.toTest = false;
//model
updateOption.saveModel = false;
updateOption.loadModel = false;
//rnn
updateOption.hiddenNums = {hiddenNum};
updateOption.hidenLayerNums = {1};
updateOption.gruCellNum = 1;
RnnAPPOUpdater<CartACGRUTruncFcNet, torch::optim::Adam> updater (
model,
optimizer,
updateOption,
q,
outputNum
);
auto updateThread = std::make_unique<std::thread>(
&RnnAPPOUpdater<CartACGRUTruncFcNet, torch::optim::Adam>::train, &updater, updateNum);
////////////////////////////////////// Test/////////////////////////////////////////
const int pollMinute = 1 * 60;
const int testBatchSize = 4;
const int testClientNum = 4;
//Test Env
std::string testAddr = addrBase + std::to_string(testPort);
LunarEnv testEnv(testAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "test env ready: " << testAddr);
at::IntArrayRef testInputShape{4};
DqnOption testOption(testInputShape, deviceType);
testOption.isAtari = false;
testOption.donePerEp = 1;
testOption.multiLifes = false;
testOption.valueCoef = 0.25;
testOption.toTest = true;
testOption.inputScale = 1;
testOption.testBatch = testBatchSize;
testOption.testEp = testBatchSize;
// testOption.rewardScale = 1;
// testOption.rewardMin = -1;
// testOption.rewardMax = 1;
testOption.tensorboardLogPath = logBase + "test" + "/" + logFileName;
AlgRNNTester<CartACGRUTruncFcNet, LunarEnv, SoftmaxPolicy> tester(model, testEnv, policy, testOption);
while (true) {
sleep(pollMinute);
tester.testAC();
}
//Never reach
for (int i = 0; i < workerNum; i ++) {
ts[i]->join();
}
updateThread->join();
LOG4CXX_INFO(logger, "End of train");
}
void testPong(const int clientNum, const int batchSize, const int roundNum, const int maxStep, const int epochNum, const int updateNum) {
//Env Config
// const int inputNum = 4;
const int outputNum = 6;
const int hiddenNum = 1024;
const int qCapacity = batchSize * roundNum * 2;
// const int maxStep = 8;
const std::string envName = "PongNoFrameskip-v4";
//Training Config
// const int roundNum = 1;
// const int maxStep = 5;
const int workerNum = roundNum;
// std::vector<float> entropyCoefs {0.01, 0.01, 0.005, 0.02, 0,02};
// assert(workerNum <= entropyCoefs.size());
const int basePort = 10201;
const int testPort = 10210;
const std::string addrBase = "tcp://127.0.0.1:";
const std::string logBase = "./logs/appornn_testpong1_log/";
const std::string logFileName = "tfevents.pb";
//Net
AirACHOGRUNet model(outputNum, hiddenNum);
model.to(deviceType);
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
SoftmaxPolicy policy(outputNum);
std::mutex updateMutex;
//Queue
AsyncRnnPPODataQ q(qCapacity);
//Workers
at::IntArrayRef inputShape{4, 84, 84};
std::vector<AirEnv*> envs;
std::vector<RnnAPPOWorker<AirACHOGRUNet, AirEnv, SoftmaxPolicy>*> workers;
for(int i = 0; i < workerNum; i ++) {
DqnOption option(inputShape, deviceType);
//env
option.envNum = clientNum;
option.isAtari = true;
option.donePerEp = 1;
option.multiLifes = false;
//grad
option.gamma = 0.99;
option.ppoLambda = 0.95;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1;
option.rewardMax = 1;
//log
option.logInterval = 100;
option.tensorboardLogPath = logBase + std::to_string(i) + "/" + logFileName;
//model
option.saveModel = false;
option.loadModel = false;
//rnn
option.hiddenNums = {hiddenNum};
option.hidenLayerNums = {1};
option.gruCellNum = 1;
option.maxStep = 8;
LOG4CXX_INFO(logger, "To put log in" << option.tensorboardLogPath);
std::string serverAddr = addrBase + std::to_string(basePort + i);
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv* env = new AirEnv(serverAddr, envName, clientNum);
env->init();
envs.push_back(env);
LOG4CXX_INFO(logger, "Env " << envName << " " << i << " ready");
RnnAPPOWorker<AirACHOGRUNet, AirEnv, SoftmaxPolicy>* worker =
new RnnAPPOWorker<AirACHOGRUNet, AirEnv, SoftmaxPolicy> (
model,
*env,
policy,
option,
q,
outputNum
);
workers.push_back(worker);
}
std::vector<std::unique_ptr<std::thread>> ts;
for (int i = 0; i < workerNum; i ++) {
ts.push_back(std::make_unique<std::thread>(
&RnnAPPOWorker<AirACHOGRUNet, AirEnv, SoftmaxPolicy>::train, &(*workers[i]), updateNum));
}
////////////////////////////////////// Updater ////////////////////////////////////////
at::IntArrayRef updateInputShape{4, 84, 84};
DqnOption updateOption(updateInputShape, deviceType);
//env
updateOption.isAtari = false;
//grad
updateOption.entropyCoef = 0.01;
updateOption.valueCoef = 0.5;
updateOption.maxGradNormClip = 0.1;
updateOption.gamma = 0.99;
//log
updateOption.logInterval = 100;
updateOption.tensorboardLogPath = logBase + "update" + "/" + logFileName;
//input
updateOption.inputScale = 255;
updateOption.rewardScale = 1;
updateOption.rewardMin = -1;
updateOption.rewardMax = 1;
//ppo
updateOption.ppoLambda = 0.95;
updateOption.ppoEpsilon = 0.1;
updateOption.appoRoundNum = roundNum;
updateOption.epochNum = epochNum;
updateOption.batchSize = batchSize;
updateOption.trajStepNum = batchSize * roundNum;
updateOption.maxKl = 0.1;
//test
updateOption.toTest = false;
//model
updateOption.saveModel = false;
updateOption.loadModel = false;
//rnn
updateOption.hiddenNums = {hiddenNum};
updateOption.hidenLayerNums = {1};
updateOption.gruCellNum = 1;
RnnAPPOUpdater<AirACHOGRUNet, torch::optim::Adam> updater (
model,
optimizer,
updateOption,
q,
outputNum
);
auto updateThread = std::make_unique<std::thread>(
&RnnAPPOUpdater<AirACHOGRUNet, torch::optim::Adam>::train, &updater, updateNum);
////////////////////////////////////// Test/////////////////////////////////////////
const int pollMinute = 4 * 60;
const int testBatchSize = 4;
const int testClientNum = 4;
//Test Env
std::string testAddr = addrBase + std::to_string(testPort);
AirEnv testEnv(testAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "test env ready: " << testAddr);
at::IntArrayRef testInputShape{4, 84, 84};
DqnOption testOption(testInputShape, deviceType);
testOption.isAtari = true;
testOption.donePerEp = 1;
testOption.multiLifes = false;
// testOption.valueCoef = 0.25;
testOption.toTest = true;
testOption.inputScale = 255;
testOption.testBatch = testBatchSize;
testOption.testEp = testBatchSize;
// testOption.rewardScale = 1;
// testOption.rewardMin = -1;
// testOption.rewardMax = 1;
testOption.tensorboardLogPath = logBase + "test" + "/" + logFileName;
AlgRNNTester<AirACHOGRUNet, AirEnv, SoftmaxPolicy> tester(model, testEnv, policy, testOption);
while (true) {
sleep(pollMinute);
tester.testAC();
}
//Never reach
for (int i = 0; i < workerNum; i ++) {
ts[i]->join();
}
updateThread->join();
LOG4CXX_INFO(logger, "End of train");
}
void testPongNr(const int clientNum, const int batchSize, const int roundNum, const int maxStep, const int epochNum, const int updateNum) {
//Env Config
// const int inputNum = 4;
const int outputNum = 6;
const int hiddenNum = 1024;
const int qCapacity = batchSize * roundNum * 2;
// const int maxStep = 8;
const std::string envName = "PongNoFrameskip-v4";
//Training Config
// const int roundNum = 1;
// const int maxStep = 5;
const int workerNum = roundNum;
// std::vector<float> entropyCoefs {0.01, 0.01, 0.005, 0.02, 0,02};
// assert(workerNum <= entropyCoefs.size());
const int basePort = 10201;
const int testPort = 10210;
const std::string addrBase = "tcp://127.0.0.1:";
const std::string logBase = "./logs/appornn_testpongnr_log/";
const std::string logFileName = "tfevents.pb";
//Net
AirACHONRGRUNet model(outputNum, hiddenNum);
model.to(deviceType);
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
SoftmaxPolicy policy(outputNum);
std::mutex updateMutex;
//Queue
AsyncRnnPPODataQ q(qCapacity);
//Workers
at::IntArrayRef inputShape{4, 84, 84};
std::vector<AirEnv*> envs;
std::vector<RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>*> workers;
for(int i = 0; i < workerNum; i ++) {
DqnOption option(inputShape, deviceType);
//env
option.envNum = clientNum;
option.isAtari = true;
option.donePerEp = 1;
option.multiLifes = false;
//grad
option.gamma = 0.99;
option.ppoLambda = 0.95;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1;
option.rewardMax = 1;
//log
option.logInterval = 100;
option.tensorboardLogPath = logBase + std::to_string(i) + "/" + logFileName;
//model
option.saveModel = false;
option.loadModel = false;
//rnn
option.hiddenNums = {hiddenNum};
option.hidenLayerNums = {1};
option.gruCellNum = 1;
option.maxStep = 8;
LOG4CXX_INFO(logger, "To put log in" << option.tensorboardLogPath);
std::string serverAddr = addrBase + std::to_string(basePort + i);
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv* env = new AirEnv(serverAddr, envName, clientNum);
env->init();
envs.push_back(env);
LOG4CXX_INFO(logger, "Env " << envName << " " << i << " ready");
RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>* worker =
new RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy> (
model,
*env,
policy,
option,
q,
outputNum
);
workers.push_back(worker);
}
std::vector<std::unique_ptr<std::thread>> ts;
for (int i = 0; i < workerNum; i ++) {
ts.push_back(std::make_unique<std::thread>(
&RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>::train, &(*workers[i]), updateNum));
}
////////////////////////////////////// Updater ////////////////////////////////////////
at::IntArrayRef updateInputShape{4, 84, 84};
DqnOption updateOption(updateInputShape, deviceType);
//env
updateOption.isAtari = false;
//grad
updateOption.entropyCoef = 0.01;
updateOption.valueCoef = 0.5;
updateOption.maxGradNormClip = 0.1;
updateOption.gamma = 0.99;
//log
updateOption.logInterval = 100;
updateOption.tensorboardLogPath = logBase + "update" + "/" + logFileName;
//input
updateOption.inputScale = 255;
updateOption.rewardScale = 1;
updateOption.rewardMin = -1;
updateOption.rewardMax = 1;
//ppo
updateOption.ppoLambda = 0.95;
updateOption.ppoEpsilon = 0.1;
updateOption.appoRoundNum = roundNum;
updateOption.epochNum = epochNum;
updateOption.batchSize = batchSize;
updateOption.trajStepNum = batchSize * roundNum;
updateOption.maxKl = 0.1;
//test
updateOption.toTest = false;
//model
updateOption.saveModel = false;
updateOption.loadModel = false;
//rnn
updateOption.hiddenNums = {hiddenNum};
updateOption.hidenLayerNums = {1};
updateOption.gruCellNum = 1;
RnnAPPOUpdater<AirACHONRGRUNet, torch::optim::Adam> updater (
model,
optimizer,
updateOption,
q,
outputNum
);
auto updateThread = std::make_unique<std::thread>(
&RnnAPPOUpdater<AirACHONRGRUNet, torch::optim::Adam>::train, &updater, updateNum);
////////////////////////////////////// Test/////////////////////////////////////////
const int pollMinute = 4 * 60;
const int testBatchSize = 4;
const int testClientNum = 4;
//Test Env
std::string testAddr = addrBase + std::to_string(testPort);
AirEnv testEnv(testAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "test env ready: " << testAddr);
at::IntArrayRef testInputShape{4, 84, 84};
DqnOption testOption(testInputShape, deviceType);
testOption.isAtari = true;
testOption.donePerEp = 1;
testOption.multiLifes = false;
// testOption.valueCoef = 0.25;
testOption.toTest = true;
testOption.inputScale = 255;
testOption.testBatch = testBatchSize;
testOption.testEp = testBatchSize;
// testOption.rewardScale = 1;
// testOption.rewardMin = -1;
// testOption.rewardMax = 1;
testOption.tensorboardLogPath = logBase + "test" + "/" + logFileName;
AlgRNNTester<AirACHONRGRUNet, AirEnv, SoftmaxPolicy> tester(model, testEnv, policy, testOption);
while (true) {
sleep(pollMinute);
tester.testAC();
}
//Never reach
for (int i = 0; i < workerNum; i ++) {
ts[i]->join();
}
updateThread->join();
LOG4CXX_INFO(logger, "End of train");
}
void testPongNrAVec(const int clientNum, const int batchSize, const int roundNum, const int maxStep, const int epochNum, const int updateNum) {
//Env Config
// const int inputNum = 4;
const int outputNum = 6;
const int hiddenNum = 1024;
const int qCapacity = batchSize * roundNum * 2;
// const int maxStep = 8;
const std::string envName = "PongNoFrameskip-v4";
//Training Config
// const int roundNum = 1;
// const int maxStep = 5;
const int workerNum = roundNum;
// std::vector<float> entropyCoefs {0.01, 0.01, 0.005, 0.02, 0,02};
// assert(workerNum <= entropyCoefs.size());
const int basePort = 10201;
const int testPort = 10210;
const std::string addrBase = "tcp://127.0.0.1:";
const std::string logBase = "./logs/appornn_testpongnravec_m16_log/";
const std::string logFileName = "tfevents.pb";
//Net
AirACHONRGRUNet model(outputNum, hiddenNum);
model.to(deviceType);
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
SoftmaxPolicy policy(outputNum);
std::mutex updateMutex;
//Queue
AsyncRnnPPODataQ q(qCapacity);
//Workers
at::IntArrayRef inputShape{4, 84, 84};
std::vector<AirEnv*> envs;
std::vector<RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>*> workers;
for(int i = 0; i < workerNum; i ++) {
DqnOption option(inputShape, deviceType);
//env
option.envNum = clientNum;
option.isAtari = true;
option.donePerEp = 1;
option.multiLifes = false;
//grad
option.gamma = 0.99;
option.ppoLambda = 0.95;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1;
option.rewardMax = 1;
//log
option.logInterval = 100;
option.tensorboardLogPath = logBase + std::to_string(i) + "/" + logFileName;
//model
option.saveModel = false;
option.loadModel = false;
//rnn
option.hiddenNums = {hiddenNum};
option.hidenLayerNums = {1};
option.gruCellNum = 1;
option.maxStep = 16;
LOG4CXX_INFO(logger, "To put log in" << option.tensorboardLogPath);
std::string serverAddr = addrBase + std::to_string(basePort + i);
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv* env = new AirEnv(serverAddr, envName, clientNum);
env->init();
envs.push_back(env);
LOG4CXX_INFO(logger, "Env " << envName << " " << i << " ready");
RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>* worker =
new RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy> (
model,
*env,
policy,
option,
q,
outputNum
);
workers.push_back(worker);
}
std::vector<std::unique_ptr<std::thread>> ts;
for (int i = 0; i < workerNum; i ++) {
ts.push_back(std::make_unique<std::thread>(
&RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>::train, &(*workers[i]), updateNum));
}
////////////////////////////////////// Updater ////////////////////////////////////////
at::IntArrayRef updateInputShape{4, 84, 84};
DqnOption updateOption(updateInputShape, deviceType);
//env
updateOption.isAtari = false;
//grad
updateOption.entropyCoef = 0.01;
updateOption.valueCoef = 0.5;
updateOption.maxGradNormClip = 0.1;
updateOption.gamma = 0.99;
//log
updateOption.logInterval = 100;
updateOption.tensorboardLogPath = logBase + "update" + "/" + logFileName;
//input
updateOption.inputScale = 255;
updateOption.rewardScale = 1;
updateOption.rewardMin = -1;
updateOption.rewardMax = 1;
//ppo
updateOption.ppoLambda = 0.95;
updateOption.ppoEpsilon = 0.1;
updateOption.appoRoundNum = roundNum;
updateOption.epochNum = epochNum;
updateOption.batchSize = batchSize;
updateOption.trajStepNum = batchSize * roundNum;
updateOption.maxKl = 0.1;
//test
updateOption.toTest = false;
//model
updateOption.saveModel = false;
updateOption.loadModel = false;
//rnn
updateOption.hiddenNums = {hiddenNum};
updateOption.hidenLayerNums = {1};
updateOption.gruCellNum = 1;
RnnAPPOUpdater<AirACHONRGRUNet, torch::optim::Adam> updater (
model,
optimizer,
updateOption,
q,
outputNum
);
auto updateThread = std::make_unique<std::thread>(
&RnnAPPOUpdater<AirACHONRGRUNet, torch::optim::Adam>::train, &updater, updateNum);
////////////////////////////////////// Test/////////////////////////////////////////
const int pollMinute = 4 * 60;
const int testBatchSize = 4;
const int testClientNum = 4;
//Test Env
std::string testAddr = addrBase + std::to_string(testPort);
AirEnv testEnv(testAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "test env ready: " << testAddr);
at::IntArrayRef testInputShape{4, 84, 84};
DqnOption testOption(testInputShape, deviceType);
testOption.isAtari = true;
testOption.donePerEp = 1;
testOption.multiLifes = false;
// testOption.valueCoef = 0.25;
testOption.toTest = true;
testOption.inputScale = 255;
testOption.testBatch = testBatchSize;
testOption.testEp = testBatchSize;
// testOption.rewardScale = 1;
// testOption.rewardMin = -1;
// testOption.rewardMax = 1;
testOption.tensorboardLogPath = logBase + "test" + "/" + logFileName;
AlgRNNTester<AirACHONRGRUNet, AirEnv, SoftmaxPolicy> tester(model, testEnv, policy, testOption);
while (true) {
sleep(pollMinute);
tester.testAC();
}
//Never reach
for (int i = 0; i < workerNum; i ++) {
ts[i]->join();
}
updateThread->join();
LOG4CXX_INFO(logger, "End of train");
}
void testBrNr(const int clientNum, const int batchSize, const int roundNum, const int maxStep, const int epochNum, const int updateNum) {
//Env Config
// const int inputNum = 4;
const int outputNum = 4;
const int hiddenNum = 1024;
const int qCapacity = batchSize * roundNum * 2;
// const int maxStep = 8;
const std::string envName = "BreakoutNoFrameskip-v4";
//Training Config
// const int roundNum = 1;
// const int maxStep = 5;
const int workerNum = roundNum;
// std::vector<float> entropyCoefs {0.01, 0.01, 0.005, 0.02, 0,02};
// assert(workerNum <= entropyCoefs.size());
const int basePort = 10201;
const int testPort = 10210;
const std::string addrBase = "tcp://127.0.0.1:";
const std::string logBase = "./logs/appornn_testbrnr_log/";
const std::string logFileName = "tfevents.pb";
//Net
AirACHONRGRUNet model(outputNum, hiddenNum);
model.to(deviceType);
torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(3e-4));
LOG4CXX_INFO(logger, "Model ready");
SoftmaxPolicy policy(outputNum);
// std::mutex updateMutex;
//Queue
AsyncRnnPPODataQ q(qCapacity);
//Workers
at::IntArrayRef inputShape{4, 84, 84};
std::vector<AirEnv*> envs;
std::vector<RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>*> workers;
for(int i = 0; i < workerNum; i ++) {
DqnOption option(inputShape, deviceType);
//env
option.envNum = clientNum;
option.isAtari = true;
option.donePerEp = 5;
option.multiLifes = true;
//grad
option.gamma = 0.99;
option.ppoLambda = 0.95;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1;
option.rewardMax = 1;
//log
option.logInterval = 100;
option.tensorboardLogPath = logBase + std::to_string(i) + "/" + logFileName;
//model
option.saveModel = false;
option.loadModel = false;
//rnn
option.hiddenNums = {hiddenNum};
option.hidenLayerNums = {1};
option.gruCellNum = 1;
option.maxStep = 8;
LOG4CXX_INFO(logger, "To put log in" << option.tensorboardLogPath);
std::string serverAddr = addrBase + std::to_string(basePort + i);
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv* env = new AirEnv(serverAddr, envName, clientNum);
env->init();
envs.push_back(env);
LOG4CXX_INFO(logger, "Env " << envName << " " << i << " ready");
RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>* worker =
new RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy> (
model,
*env,
policy,
option,
q,
outputNum
);
workers.push_back(worker);
}
std::vector<std::unique_ptr<std::thread>> ts;
for (int i = 0; i < workerNum; i ++) {
ts.push_back(std::make_unique<std::thread>(
&RnnAPPOWorker<AirACHONRGRUNet, AirEnv, SoftmaxPolicy>::train, &(*workers[i]), updateNum));
}
////////////////////////////////////// Updater ////////////////////////////////////////
at::IntArrayRef updateInputShape{4, 84, 84};
DqnOption updateOption(updateInputShape, deviceType);
//env
updateOption.isAtari = true;
//grad
updateOption.entropyCoef = 0.01;
updateOption.valueCoef = 0.5;
updateOption.maxGradNormClip = 0.1;
updateOption.gamma = 0.99;
//log
updateOption.logInterval = 100;
updateOption.tensorboardLogPath = logBase + "update" + "/" + logFileName;
//input
updateOption.inputScale = 255;
updateOption.rewardScale = 1;
updateOption.rewardMin = -1;
updateOption.rewardMax = 1;
//ppo
updateOption.ppoLambda = 0.95;
updateOption.ppoEpsilon = 0.1;
updateOption.appoRoundNum = roundNum;
updateOption.epochNum = epochNum;
updateOption.batchSize = batchSize;
updateOption.trajStepNum = batchSize * roundNum;
updateOption.maxKl = 0.1;
//test
updateOption.toTest = false;
//model
updateOption.saveModel = false;
updateOption.loadModel = false;
//rnn
updateOption.hiddenNums = {hiddenNum};
updateOption.hidenLayerNums = {1};
updateOption.gruCellNum = 1;
RnnAPPOUpdater<AirACHONRGRUNet, torch::optim::Adam> updater (
model,
optimizer,
updateOption,
q,
outputNum
);
auto updateThread = std::make_unique<std::thread>(
&RnnAPPOUpdater<AirACHONRGRUNet, torch::optim::Adam>::train, &updater, updateNum);
////////////////////////////////////// Test/////////////////////////////////////////
const int pollMinute = 4 * 60;
const int testBatchSize = 4;
const int testClientNum = 4;
//Test Env
std::string testAddr = addrBase + std::to_string(testPort);
AirEnv testEnv(testAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "test env ready: " << testAddr);
at::IntArrayRef testInputShape{4, 84, 84};
DqnOption testOption(testInputShape, deviceType);
testOption.isAtari = true;
testOption.donePerEp = 5;
testOption.multiLifes = true;
// testOption.valueCoef = 0.25;
testOption.toTest = true;
testOption.inputScale = 255;
testOption.testBatch = testBatchSize;
testOption.testEp = testBatchSize;
// testOption.rewardScale = 1;
// testOption.rewardMin = -1;
// testOption.rewardMax = 1;
testOption.tensorboardLogPath = logBase + "test" + "/" + logFileName;
AlgRNNTester<AirACHONRGRUNet, AirEnv, SoftmaxPolicy> tester(model, testEnv, policy, testOption);
while (true) {
sleep(pollMinute);
tester.testAC();
}
//Never reach
for (int i = 0; i < workerNum; i ++) {
ts[i]->join();
}
updateThread->join();
LOG4CXX_INFO(logger, "End of train");
}
void testBrNrAVec(const int clientNum, const int batchSize, const int roundNum, const int maxStep, const int epochNum, const int updateNum) {
//Env Config
// const int inputNum = 4;