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testsac.cpp
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/*
* testsac.cpp
*
* Created on: Sep 17, 2021
* Author: zf
*/
#include "alg/cnn/sac.hpp"
#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/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("sactest"));
const torch::Device deviceType = torch::kCUDA;
void testProbe(const int epochNum) {
const int batchSize = 1;
const int inputNum = 4;
const int envId = 5;
const int outputNum = 2;
const int envNum = batchSize;
ProbeEnvWrapper env(inputNum, envId, envNum);
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");
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(0.005));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(0.005));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(0.005));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(1e-4));
LOG4CXX_INFO(logger, "Model ready");
at::IntArrayRef inputShape{envNum, 4};
DqnOption option(inputShape, deviceType);
option.envNum = envNum;
//target model
option.targetUpdateStep = 100;
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 = 0.95;
//grad
option.batchSize = 4;
option.startStep = 50;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testprobe";
//model
option.saveModel = false;
option.savePathPrefix = "./sac_testprobe";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = false;
//sac
option.targetEntropy = -0.98 * std::log(1.0f / (float)outputNum);
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)
*/
Sac<CartSacQNet, CartSacPolicyNet, ProbeEnvWrapper, SoftmaxPolicy, torch::optim::Adam, torch::optim::Adam, torch::optim::Adam>
sac(model1, model2, targetModel1, targetModel2, optimizerQ1, optimizerQ2,
pModel, optimizerP,
logAlpha, optimizerA,
env, policy,
env,
deviceType, option);
sac.train(epochNum);
// dqn.train(epochNum);
}
void testCart(const int epochNum) {
const std::string envName = "CartPole-v0";
const int clientNum = 1; //8
const int outputNum = 2;
const int inputNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
LunarEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10205";
LOG4CXX_DEBUG(logger, "To connect to " << testServerAddr);
LunarEnv testEnv(testServerAddr, envName, clientNum);
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 = -1000; //TODO: reward may not require clip
option.rewardMax = 1000;
option.gamma = 0.98;
//grad
option.batchSize = 4;
option.startStep = 50;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testcart";
option.logInterval = 100;
//model
option.saveModel = false;
option.savePathPrefix = "./sac_testcart";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 1000;
option.testEp = 8;
//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)
*/
Sac<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 testtestCart(const int epochNum) {
const std::string envName = "CartPole-v0";
const int clientNum = 1; //8
const int outputNum = 2;
const int inputNum = 4;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
LunarEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "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");
torch::optim::Adam optimizerQ1(model1.parameters(), torch::optim::AdamOptions(0.005));
torch::optim::Adam optimizerQ2(model2.parameters(), torch::optim::AdamOptions(0.005));
LOG4CXX_INFO(logger, "Q optimizer ready");
torch::optim::Adam optimizerP(pModel.parameters(), torch::optim::AdamOptions(0.005));
torch::optim::Adam optimizerA({logAlpha}, torch::optim::AdamOptions(1e-4));
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 = 100;
//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 = 0.95;
//grad
option.batchSize = 4;
option.startStep = 50;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testcart";
//model
option.saveModel = false;
option.savePathPrefix = "./sac_testcart";
option.loadModel = true;
option.loadOptimizer = false;
option.loadPathPrefix = "/home/zf/workspaces/workspace_cpp/rlpractice/build/test/gymtest/sac_testcart";
//sac
option.targetEntropy = -0.98 * std::log(1.0f / (float)outputNum);
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)
*/
Sac<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,
env,
deviceType, option);
sac.test(epochNum);
}
void testLog(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10201";
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 rl = 3e-4;
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));
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 = 80000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
//TODO: inputScale = 255;
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -1; //TODO: reward may not require clip
option.rewardMax = 1;
option.gamma = 0.99;
//grad
option.batchSize = 4;
option.startStep = 1000;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testlog";
option.logInterval = 100;
//model
option.saveModel = false;
option.savePathPrefix = "./sac_testlog";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 8;
//sac
option.targetEntropy = -0.98 * std::log(1.0f / (float)outputNum);
option.envStep = 4;
SoftmaxPolicy policy(outputNum);
Sac<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 testPacman(const int epochNum) {
const std::string envName = "MsPacmanNoFrameskip-v4";
const int clientNum = 1; //8
const int outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10201";
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");
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 = 80000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -100; //TODO: reward may not require clip
option.rewardMax = 100;
option.gamma = 0.99;
//grad
option.batchSize = 64;
option.startStep = 10000;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testpacman";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./sac_testpacman";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 8;
//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)
*/
Sac<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 outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10201";
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");
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 = 80000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 2;
option.rewardMin = -100; //TODO: reward may not require clip
option.rewardMax = 100;
option.gamma = 0.99;
//grad
option.batchSize = 64;
option.startStep = 10000;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testpacman1";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./sac_testpacman1";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 8;
//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)
*/
Sac<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 outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10201";
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");
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 = 80000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1.5;
option.rewardMin = -100; //TODO: reward may not require clip
option.rewardMax = 100;
option.gamma = 0.99;
//grad
option.batchSize = 64;
option.startStep = 10000;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testpacman2";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./sac_testpacman2";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 8;
//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)
*/
Sac<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 outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10201";
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");
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 = 80000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -100; //TODO: reward may not require clip
option.rewardMax = 100;
option.gamma = 0.99;
//grad
option.batchSize = 64;
option.startStep = 10000;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testpacman3";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./sac_testpacman3";
option.loadModel = true;
option.loadOptimizer = true;
option.loadPathPrefix = "/home/zf/workspaces/workspace_cpp/rlpractice/build/test/gymtest/sac_testpacman";
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 8;
//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)
*/
Sac<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 outputNum = 9;
std::string serverAddr = "tcp://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10201";
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");
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 = 80000;
//explore
option.exploreBegin = 0;
option.exploreEnd = 0;
option.explorePart = 1;
//input
option.inputScale = 255;
option.rewardScale = 1;
option.rewardMin = -100; //TODO: reward may not require clip
option.rewardMax = 100;
option.gamma = 0.99;
//grad
option.batchSize = 64;
option.startStep = 10000;
option.maxGradNormClip = 1;
//log
option.statCap = 128;
option.statPathPrefix = "./sac_testpacman4";
option.logInterval = 100;
//model
option.saveModel = true;
option.savePathPrefix = "./sac_testpacman4";
option.loadModel = false;
option.loadOptimizer = false;
//test
option.toTest = true;
option.testGapEp = 10000;
option.testEp = 8;
//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)
*/
Sac<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://127.0.0.1:10203";
LOG4CXX_DEBUG(logger, "To connect to " << serverAddr);
AirEnv env(serverAddr, envName, clientNum);
env.init();
LOG4CXX_INFO(logger, "Env " << envName << " ready");
std::string testServerAddr = "tcp://127.0.0.1:10201";
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