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testsaczip.cpp
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/*
* testsaczip.cpp
*
* Created on: Oct 12, 2021
* Author: zf
*/
#include "alg/cnn/saczip.hpp"
#include "alg/cnn/sacnopolicy.hpp"
#include "gymtest/env/airenv.h"
#include "gymtest/env/lunarenv.h"
#include "gymtest/cnnnets/airnets/aircnnnet.h"
#include "gymtest/cnnnets/airnets/airacbmnet.h"
#include "gymtest/cnnnets/airnets/airacnet.h"
#include "gymtest/cnnnets/airnets/airdueling.h"
#include "gymtest/cnnnets/airnets/airacbmsmallkernelnet.h"
#include "gymtest/cnnnets/lunarnets/cartacnet.h"
#include "gymtest/cnnnets/lunarnets/cartnet.h"
#include "gymtest/cnnnets/lunarnets/cartduelnet.h"
#include "gymtest/cnnnets/lunarnets/cartsacqnet.h"
#include "gymtest/cnnnets/lunarnets/cartsacpolicynet.h"
#include "gymtest/cnnnets/airnets/airachonet.h"
#include "gymtest/cnnnets/airnets/airsacqnet.h"
#include "gymtest/cnnnets/airnets/airsacpnet.h"
#include "gymtest/cnnnets/airnets/airsacqb1net.h"
#include "gymtest/noisynets/noisycartfcnet.h"
#include "gymtest/noisynets/noisyaircnnnet.h"
#include "gymtest/train/rawpolicy.h"
#include "gymtest/train/softmaxpolicy.h"
#include "probeenvs/ProbeEnvWrapper.h"
#include <torch/torch.h>
#include <log4cxx/logger.h>
#include <log4cxx/basicconfigurator.h>
#include <log4cxx/consoleappender.h>
#include <log4cxx/simplelayout.h>
#include <log4cxx/logmanager.h>
#include <vector>
#include <cmath>
#include "alg/utils/dqnoption.h"
namespace {
log4cxx::LoggerPtr logger(log4cxx::Logger::getLogger("sacziptest"));
const torch::Device deviceType = torch::kCUDA;
//Zip is not good for Cart as input is of float instead of int
void testZipCart(const int epochNum) {
const std::string envName = "CartPole-v0";
const int clientNum = 1; //8
const int testClientNum = 1;
const int outputNum = 2;
const int inputNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10201";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
LunarEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
LunarEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
CartSacQNet model1(inputNum, outputNum);
model1.to(deviceType);
CartSacQNet model2(inputNum, outputNum);
model2.to(deviceType);
CartSacQNet targetModel1(inputNum, outputNum);
targetModel1.to(deviceType);
CartSacQNet targetModel2(inputNum, outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
CartSacPolicyNet pModel(inputNum, outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
float rl = 0.003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(rl));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(rl));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(rl));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(rl)); //rl: 46, rl / 2: 349, rl * 2: > 350
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
option.targetUpdateStep = 1000;
option.tau = 1;
//buffer
option.rbCap = 10000;
//explore
// option.exploreBegin = 0;
// option.exploreEnd = 0;
// option.explorePart = 1;
//input
option.inputScale = 1;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.98;
//grad
option.batchSize = 4;
option.startStep = 50;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
// option.statPathPrefix = "./saczip_testcart";
option.tensorboardLogPath = "./logs/saczip_testcart/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = false;
option.savePathPrefix = "./saczip_testcart";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = false;
option.testGapEp = 1000;
option.testEp = 4;
option.testBatch = testClientNum;
//sac
option.targetEntropy = -0.98 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<CartSacQNet, CartSacPolicyNet, LunarEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman0(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int testClientNum = 1;
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
torch::manual_seed(0);
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
//TODO: increase update gap
option.targetUpdateStep = 8000;
option.tau = 1;
//buffer
option.rbCap = 300000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//output
option.outputNum = outputNum;
//grad
option.batchSize = 64;
option.startStep = 20000;
option.maxGradNormClip = 1000;
//log
option.statCap = 128;
option.tensorboardLogPath = "./logs/saczip_testpac0/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./saczip_testpacman0";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 3;
option.testBatch = testClientNum;
option.livePerEpisode = 3;
//sac
option.targetEntropy = -0.98 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<AirSacQNet, AirSacPNet, AirEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman01(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 4; //8
const int testClientNum = 9;
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
torch::manual_seed(0);
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
//TODO: increase update gap
option.targetUpdateStep = 6000;
option.tau = 1;
//buffer
option.rbCap = 300000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 2;
option.rewardMin = -2; //TODO: reward may not require clip
option.rewardMax = 2;
option.gamma = 0.99;
//output
option.outputNum = outputNum;
//grad
option.batchSize = 64;
option.startStep = 5000;
option.maxGradNormClip = 1000;
//log
option.statCap = 128;
option.tensorboardLogPath = "./logs/saczip_testpac01/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./saczip_testpacman01";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 9;
option.testBatch = testClientNum;
option.livePerEpisode = 3;
//sac
option.targetEntropy = -0.98 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<AirSacQNet, AirSacPNet, AirEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman1(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int testClientNum = 9;
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
torch::manual_seed(0);
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
//TODO: increase update gap
option.targetUpdateStep = 8000;
option.tau = 1;
//buffer
option.rbCap = 300000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//output
option.outputNum = outputNum;
//grad
option.batchSize = 64;
option.startStep = 20000;
option.maxGradNormClip = 1000;
//log
option.statCap = 128;
option.tensorboardLogPath = "./logs/saczip_testpac1/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./saczip_testpacman1";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 18;
option.testBatch = testClientNum;
option.livePerEpisode = 3;
//sac
option.fixedEntropy = true;
option.targetEntropy = -0.90 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<AirSacQNet, AirSacPNet, AirEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman11(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int testClientNum = 9;
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
torch::manual_seed(0);
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
//TODO: increase update gap
option.targetUpdateStep = 8000;
option.tau = 1;
//buffer
option.rbCap = 300000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 2;
option.rewardMin = -2; //TODO: reward may not require clip
option.rewardMax = 2;
option.gamma = 0.99;
//output
option.outputNum = outputNum;
//grad
option.batchSize = 64;
option.startStep = 20000;
option.maxGradNormClip = 1000;
//log
option.statCap = 128;
option.tensorboardLogPath = "./logs/saczip_testpac11/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./saczip_testpacman11";
option.saveThreshold = 1000;
option.saveStep = 100;
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 18;
option.testBatch = testClientNum;
option.livePerEpisode = 3;
//sac
option.fixedEntropy = true;
option.targetEntropy = -0.90 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<AirSacQNet, AirSacPNet, AirEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman2(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int testClientNum = 9;
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
torch::manual_seed(0);
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
//TODO: increase update gap
option.targetUpdateStep = 8000;
option.tau = 1;
//buffer
option.rbCap = 300000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//output
option.outputNum = outputNum;
//grad
option.batchSize = 64;
option.startStep = 20000;
option.maxGradNormClip = 1000;
//log
option.statCap = 128;
option.tensorboardLogPath = "./logs/saczip_testpac2/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./saczip_testpacman2";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 18;
option.testBatch = testClientNum;
option.livePerEpisode = 3;
//sac
option.fixedEntropy = true;
option.targetEntropy = -0.95 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<AirSacQNet, AirSacPNet, AirEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman3(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int testClientNum = 9;
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
torch::manual_seed(0);
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
//TODO: increase update gap
option.targetUpdateStep = 8000;
option.tau = 1;
//buffer
option.rbCap = 300000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//output
option.outputNum = outputNum;
//grad
option.batchSize = 64;
option.startStep = 20000;
option.maxGradNormClip = 1000;
//log
option.statCap = 128;
option.tensorboardLogPath = "./logs/saczip_testpac3/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./saczip_testpacman3";
option.saveThreshold = 1000;
option.saveStep = 100;
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 18;
option.testBatch = testClientNum;
option.livePerEpisode = 3;
//sac
option.fixedEntropy = true;
option.targetEntropy = -0.80 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<AirSacQNet, AirSacPNet, AirEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman4(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int testClientNum = 8;
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, testClientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{clientNum, 4, 84, 84};
DqnOption option(inputShape, deviceType);
option.envNum = clientNum;
//target model
//TODO: increase update gap
option.targetUpdateStep = 8000;
option.tau = 1;
//buffer
option.rbCap = 300000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//output
option.outputNum = outputNum;
//grad
option.batchSize = 64;
option.startStep = 20000;
option.maxGradNormClip = 1000;
//log
option.statCap = 128;
option.tensorboardLogPath = "./logs/saczip_testpac4/tfevents.pb";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./saczip_testpacman4";
option.loadModel = true;
option.loadOptimizer = true;
option.loadPathPrefix = "/home/zf/workspaces/workspace_cpp/rlpractice/build/test/gymtest/saczip_testpacman3";
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 40;
option.testBatch = testClientNum;
option.testRender = false;
//sac
option.targetEntropy = -0.90 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
/*
* Sac<QNetType, PNetType, EnvType, PolicyType, QOptimizerType, POptimizerType, AlphaOptimizerType>::Sac(
QNetType& qModel1, QNetType& qModel2, QNetType& qTargetModel1, QNetType& qTargetModel2, QOptimizerType& qOpt1, QOptimizerType& qOpt2,
PNetType& pModel, POptimizerType& pOpt,
torch::Tensor& iLogAlpha, AlphaOptimizerType& aOpt,
EnvType& iEnv, PolicyType iPolicy,
const torch::Device dType, DqnOption iOption)
*/
SacZip<AirSacQNet, AirSacPNet, AirEnv, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
testEnv,
deviceType, option);
sac.train(epochNum);
}
void testPacman5(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10207";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
AirEnv testEnv(testServerAddr, envName, clientNum);
testEnv.init();
LOG4CXX_INFO(logger, "Test env " << envName << " ready");
AirSacQNet model1(outputNum);
model1.to(deviceType);
AirSacQNet model2(outputNum);
model2.to(deviceType);
AirSacQNet targetModel1(outputNum);
targetModel1.to(deviceType);
AirSacQNet targetModel2(outputNum);
targetModel2.to(deviceType);
LOG4CXX_INFO(logger, "Q models ready");
AirSacPNet pModel(outputNum);
pModel.to(deviceType);
LOG4CXX_INFO(logger, "Policy model ready");
torch::Tensor logAlpha = torch::zeros({1}, torch::TensorOptions().requires_grad(true).device(deviceType));
LOG4CXX_INFO(logger, "Alpha ready");
//TODO: decrease lr
float lr = 0.0003;
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(lr));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(lr));
LOG4CXX_INFO(logger, "Model ready");